Correlation between laboratory
characteristics and clinical degree of dengue as an initial stage in a development of
machine learning predictor program
Cite as: AIP Conference Proceedings 2264, 030008 (2020); https://doi.org/10.1063/5.0023932 Published Online: 22 September 2020
Permatasari Silitonga, Beti E. Dewi, Alhadi Bustamam, et al.
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Correlation Between Laboratory Characteristics and Clinical Degree of Dengue as an Initial Stage in a Development of Machine Learning Predictor Program
Permatasari Silitonga
1a), Beti E. Dewi
2b), Alhadi Bustamam
1c), and Titin Siswantining
1d)1Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Kampus Baru UI, Depok 16424, Indonesia
2Department of Microbiology, Faculty of Medicine, Universitas Indonesia, Jl. Salemba Raya No.5, Kota Jakarta Pusat, Daerah Khusus Ibukota Jakarta 10430, Indonesia
a)Corresponding author: permatasari.silitonga@sci.ui.ac.id
b)betied@yahoo.com
c)alhadi@sci.ui.ac.id
d)titin@sci.ui.ac.id
Abstract. Dengue is one of the endemic diseases in Indonesia. Dengue is being suffered by many people, regardless of their gender and age. Therefore, a research about dengue based on a dengue patients’ data is conducted. There was a lot of information written in that data regarding the corresponding patients and the dengue they had suffered, such as gender, age, how long the patients were hospitalized, the symptoms they experienced, and laboratory characteristics. The diagnosis of each of the corresponding patients based on the symptoms and laboratory characteristics were also written in that data. The diagnoses were classified into three different clinical degrees according to the severity level, which are DF as the mild level, DHF grade 1 as the intermediate level, and DHF grade 2 as the severe level. In this research, data of the patients on the third day of being hospitalized is analyzed, because on the third day, dengue is entering a critical phase. The objectives of this research are: i) to find laboratory characteristics that affect the clinical degree of dengue in the critical phase, and ii) to analyze how robust the impact of those laboratory characteristics on the clinical degree of dengue in the critical phase.
In this research, Bivariate Analysis was applied as the method to find the solution of the analyzed problems. The results obtained from this research can give information for the physicians about laboratory characteristics that affect the clinical degree of dengue in the critical phase, and how robust the impact of those laboratory characteristics on the clinical degree of dengue in the critical phase. Those results also can help the physicians to find solutions or strategies in preventing and/or treating dengue. Furthermore, those results will be used in the development of Machine Learning predictor program which will be able to predict the clinical degree of dengue in the critical phase, if the laboratory characteristics are known.
Keywords: dengue, laboratory characteristics, clinical degree, diagnosis, bivariate analysis
INTRODUCTION
Dengue is a disease caused by dengue virus. Dengue virus is a part of the family Flaviviridae and genus Flavivirus [1]. Dengue virus is transmitted to humans through female mosquitoes that are infected by the virus itself. Dengue virus is transmitted by Aedes aegypti female mosquitoes as the primer vector and Aedes albopictus female mosquitoes as the secondary vector [3].
If someone is suffering from dengue, it can be said that that person is infected by dengue virus. If a healthy Ae.
aegypti or Ae. albopictus female mosquito bites an infected person, the mosquito will become infected. Furthermore,
Symposium on BioMathematics 2019 (SYMOMATH 2019) AIP Conf. Proc. 2264, 030008-1–030008-10; https://doi.org/10.1063/5.0023932
Published by AIP Publishing. 978-0-7354-2024-3/$30.00
if that mosquito bites another person, that person will become infected as well. So, Ae. aegypti or Ae. albopictus female mosquitoes can be the carrier, and also the transmitter of dengue virus. Dengue virus has an incubation period for 4- 10 days [4]. After the incubation period ends, an infected mosquito can transmit dengue virus in its lifetime.
Dengue virus consists of four serotypes, which is DENV-1, DENV-2, DENV-3, and DENV-4. A person who is infected by dengue virus can be infected by one serotype of dengue virus or more [7]. For example, a person can be infected only by DENV-3, or by DENV-2 and DENV-3 simultaneously, and so on. Each infection caused by dengue virus can have different clinical manifestations, so it is very hard to determine specific clinical symptoms and laboratory characteristics for each serotype.
Dengue has three different clinical degrees according to its severity level, which is DF (Dengue Fever), DHF (Dengue Hemorrhagic Fever), dan DSS (Dengue Shock Syndrome) [5]. Dengue cases are commonly found in tropical and subtropical countries, such as Southeast Asia, South America, and many more. Generally, dengue cases happen in urban and suburban areas [2], and frequently appear in rainy season.
Dengue is one of the endemic diseases in Indonesia. In 1968, dengue was first found in Indonesia, in the city of Jakarta and Surabaya [6]. The presence of dengue virus had caused Indonesia the greatest economic lost in Southeast Asia region. Similar to most of the other Southeast Asian countries, treatment of dengue fever in Indonesia is still considered ineffective.
According to WHO, Indonesia is a country with the highest amount of dengue patients in Southeast Asia (WHO, 2012). In 2013, the number of reported dengue patients in Indonesia was 112,511 persons, with the number of deaths was 871 persons (Incidence Rate (IR) = 45.85% per 100,000 citizens and Case Fatality Rate (CFR) = 0.77%) [6]. In 2014, the number of reported dengue patients in Indonesia was 100,347 persons (IR = 39.8% and CFR = 0.9%). In 2015, the number of reported dengue patients in Indonesia was 129,650 persons, with the number of deaths was 1,071 persons (IR = 50.75% and CFR = 0.83%) [11].
In this research, dengue is analyzed based on dengue patients’ data from the year of 2009. There was a lot of information written in that data regarding the corresponding patients and the dengue they had suffered, such as gender, age, how long the patients were hospitalized, the symptoms they experienced, and laboratory characteristics. The diagnosis of each of the corresponding patients based on the symptoms and laboratory characteristics were also written in that data. The diagnosis is classified into three different clinical degrees according to its severity level, which is DF as the mild level, DHF grade 1 as the intermediate level, and DHF grade 2 as the severe level. Laboratory characteristics are defined as the independent variables, while the diagnosis is defined as the dependent variable.
As explained previously, dengue has three different clinical degrees according to its severity level, which is DF, DHF, dan DSS. But in this research, clinical degrees that are analyzed are only DF and DHF, where DHF is divided into two different degrees, which is DHF grade 1 and DHF grade 2.
There was a lot of independent variables which were measured for ten times in the dengue patients’ data 2009.
Ten times measurements mean the corresponding independent variables were being observed for ten days.
Nevertheless, variables used in this research are only variables on the third day of observation, which is variables from the patients’ data on the third day of being hospitalized. It is because on the third day, dengue patients are entering the critical phase (critical phase of dengue occurs on day fourth to sixth). Those variables are only analyzed when the patients are entering the critical phase because results obtained from this research will be used in the development of Machine Learning predictor program which will be able to predict the clinical degree of dengue in the critical phase, if the laboratory characteristics are known.
Laboratory characteristics that are used in this research consist of Fibrinogen, Hemoglobin, Hematocrit, Leukocyte, Thrombocyte (platelet count), Neutrophil, Lymphocyte, and Monocyte. Some of the other laboratory characteristics were only measured on the first, fourth or fifth, and seventh day of observation. Because they were not measured on the third day of observation, they are not analyzed in this research. Below is some brief explanation of some of the laboratory characteristics analyzed in this research:
1. Hematocrit. Hematocrit is the concentrate (stated in percentage) of red blood cell or erythrocyte in 100 mL whole blood. Hematocrit is used to measure blood consistency. The amount of hematocrit will increase (until it reaches hemoconcentration: a state where the amount of hematocrit is above normal) due to increasing level of blood cell or decreasing volume of blood plasma. Oppositely, the amount of hematocrit will decrease (until it reaches hemodilution: a state where the amount of hematocrit is below normal) due to decreasing level of blood cell or increasing volume of blood plasma.
Generally, the volume of blood plasma of dengue patients decreases due to plasma leakage. The decreasing volume of blood plasma conduce hemoconcentration. Hemoconcentration itself is characterized by the increasing amount of hematocrit. Therefore, hematocrit is one of the important laboratory characteristics to be analyzed in this research.
2. Hemoglobin. Hemoglobin is a protein in red blood cell which carries oxygen to the whole body. Measuring hemoglobin is one of the ways to observe the beginning and the development of plasma leakage in dengue patients – one of the characteristics of plasma leakage is the amount of hemoglobin is above normal (WHO, 1997). This measurement indicates the permeability of small blood vessels of dengue patients.
Based on the research conducted by Ibrahim et al (2003a), hematocrit is not a very assuring measurement to describe the condition of dengue patients with a degree of DF or DHF. Therefore, hemoglobin is chosen to describe the condition of dengue patients with a degree of DF or DHF, because hemoglobin is considered more accurate (WHO, 1997).
In the febrile phase of dengue, the amount of hemoglobin is usually normal to low. Then, it will increase following the increase of hemoconcentration. The increasing amount of hemoglobin is the first hematology abnormality which is found in dengue cases. Therefore, hemoglobin is one of the important laboratory characteristics to be analyzed in this research.
3. Leukocyte. Leukocyte is the white blood cell. While leucopenia is a state where the amount of leukocyte in human body is below normal. Leucopenia increases the risk of having an infection, which one of them is dengue infection.
Generally, dengue patients suffer from leucopenia. They start to suffer leucopenia in the febrile phase.
Leucopenia reaches its peak a moment before the fever decreases, when the patient is entering the critical phase. In other words, the amount of leukocyte in a dengue patient is decreasing, and it reaches the lowest point when the patient is entering the critical phase. A day after the leukocyte reaches its lowest point, the fever will decrease and the patient is in the critical phase. After the fever decreases, the amount of leukocyte increases and goes back to normal. Therefore, leukocyte is one of the important laboratory characteristics to be analyzed in this research.
4. Thrombocyte (platelet count). Thrombocyte is the blood cell which is produced inside the bone marrow.
Thrombocyte is commonly called platelets. The normal count of platelets is about 250 x 10 2 9/l (in the range of 150 – 400 x 109/l) and the lifespan of a normal platelet is 7-10 days. The main function of platelets is to stop the bleeding. Without platelets, there can be a spontaneous blood leakage through the small blood vessels.
Decreasing count of platelets to less than 150,000/µl is categorized as thrombocytopenia. Thrombocytopenia can also be defined as a state where the count of platelets in human body is below normal. Thrombocytopenia can conduce hemorrhage, such as petechiae, ecchymosis, gum bleeding, epistaxis, hematemesis, and melena.
Generally, dengue patients suffer from thrombocytopenia, so it would be easier for them to suffer from bleedings. In other words, dengue patients have a decreasing count of platelets (platelets decreasing to less than 100,000mm3 are usually found on day third and eighth of being hospitalized). In dengue cases, thrombocytopenia is caused by the formation of complex antibody viruses which stimulate the aggregation of platelets. Those aggregates pass through reticulo endothelial system (RES), so they were destroyed by the RES. As the result, the count of platelets decreases. Furthermore, this conduces thrombocytopenia in dengue patients. Therefore, platelet count is one of the important laboratory characteristics to be analyzed in this research.
The other laboratory characteristics, such as Fibrinogen, Neutrophil, Lymphocyte, and Monocyte, have not been analyzed in the previous researches, regarding their correlation with the clinical degree of dengue. Nevertheless, the correlation between those laboratory characteristics on the third day of observation and the clinical degree of dengue is being analyzed in this research. In this research, correlation is defined as an impact where the laboratory characteristics on the third day of observation affect the clinical degree of dengue in the critical phase. If there is a correlation between them – in another word, impact, the robustness of the impact of those laboratory characteristics on the clinical degree of dengue in the critical phase will be analyzed.
Therefore, the purposes of this research are to find laboratory characteristics that affect the clinical degree of dengue in the critical phase and to analyze how robust the impact of those laboratory characteristics on the clinical degree of dengue in the critical phase. The benefits of knowing this information are as follows:
1. Give information for the physicians about laboratory characteristics that affect the clinical degree of dengue in the critical phase, and how robust the impact of those laboratory characteristics on the clinical degree of dengue in the critical phase.
2. Help the physicians to find solutions or strategies in preventing and/or treating dengue.
3. The results obtained from this research will be used in the development of Machine Learning predictor program which will be able to predict the clinical degree of dengue in the critical phase, if the laboratory characteristics are known. Laboratory characteristics that affect the clinical degree of dengue in the critical phase will be used as predictors of dengue, in which the values will be the input values of the program. After
the values of the predictors are entered, the program will predict the clinical degree of dengue based on those values. The output of the program will be the clinical degree of dengue itself.
METHODS
Bivariate Analysis (BA) is a statistical method that is used to analyze the relationship between independent and dependent variables using statistical test(s). There’s a lot of statistical tests in BA, such as Kendall’s Tau correlation test, Pearson’s correlation test, Spearman’s rank correlation test, and many more.
Besides being used in mathematical and statistical research, BA is frequently used in medical or clinical research.
In clinical research, independent variables that are generally analyzed are treatment received by the patients, clinical symptoms of the patients suffering from a particular disease, patients’ laboratory test results, and other things that are considered able to predict the value of the dependent variable(s). Meanwhile, dependent variables that are generally analyzed are the type of disease suffered by the patients based on their clinical symptoms, clinical degree of the disease suffered by the patients based on their clinical symptoms and/or laboratory characteristics, and other things which the value or outcome depends on the independent variables that are analyzed in the corresponding research.
In this research, BA is used to analyze the relationship between laboratory characteristics of dengue patients as the independent variables, and clinical degree of dengue as the dependent variable. A statistical test that is used in this research is Spearman’s rank correlation test.
Spearman’s rank correlation test is one of the statistical test techniques in BA that is used to analyze the strength and the direction of a monotonic relationship between two variables. Monotonic relationship itself can be defined as a relationship that meets one of these conditions: (1) monotonically increasing: if the value of one variable increases, then the value of another variable never decreases; or (2) monotonically decreasing: if the value of one variable increases, then the value of another variable never increases.
In Spearman’s rank correlation test, the type of the analyzed variables has to be ordinal. If otherwise, then the variables have to be changed into ordinal type, that is, by ranking them.
In Spearman’s rank correlation test, there is Spearman’s correlation coefficient which is denoted by 𝜌. The value of 𝜌 indicates the strength and the direction of a monotonic relationship between two variables. It lies between -1 to +1. The value of +1 indicates a perfect positive monotonic relationship, 0 indicates that there is no monotonic relationship, and -1 indicates a perfect negative monotonic relationship. The closer its value to 0, the weaker monotonic relationship between the two variables. The closer its value to 1, the stronger monotonic relationship between the two variables. The positive (+) or negative (-) sign indicates the direction of the monotonic relationship between two variables. Positive sign indicates that the monotonic relationship exists in a same direction, while negative sign indicates that the monotonic relationship exists in an opposite direction. Same-direction monotonic relationship is achieved when the value of one variable increases, the value of another variable also increases.
Opposite-direction monotonic relationship is achieved when the value of one variable increases, the value of another variable decreases.
There are two formulas to calculate Spearman’s correlation coefficient for 𝑛 ≤ 30, depends whether: (1) the data doesn’t have tied ranks; or (2) the data has tied ranks. If the data doesn’t have tied ranks, the formula is
𝜌 = 1 −
6 ∑ 𝑑𝑖2𝑛(𝑛2−1) (1)
where 𝑑𝑖 is calculated by subtracting the rank of Yi from the rank of Xi, and 𝑛 = number of cases.
If the data has a small proportion of tied ranks, those tied ranks can be neglected and the correlation coefficient can be calculated with formula (1). Nevertheless, if the data has a large proportion of tied ranks, those tied ranks cannot be neglected. There is a correction factor (T), which the formula is
𝑇 =𝑡3− 𝑡 12
where 𝑡 = the number of observations that are tied for some particular rank. When there is a correction factor, the correlation coefficient has to be calculated using the formula below
𝜌 =
∑ 𝑥2+∑ 𝑦2−∑ 𝑑𝑖22√∑ 𝑥2∑ 𝑦2 (2)
with
∑ 𝑥
2 =𝑛3− 𝑛12 −
∑
𝑇𝑥∑ 𝑦
2 =𝑛3− 𝑛12 −
∑
𝑇𝑦where 𝑇𝑥 = the sum of the values of T for the various tied ranks in X, and 𝑇𝑦 = the sum of the values of T for the various tied ranks in Y.
More specifically, below are the interpretation of 𝜌 value:
• 𝜌 = 0 no correlation/monotonic relationship.
• 0 < 𝜌 < 0,2 very weak correlation/monotonic relationship.
• 0,2 ≤ 𝜌 < 0,4 weak correlation/monotonic relationship.
• 0,4 ≤ 𝜌 < 0,6 moderate correlation/monotonic relationship.
• 0,6 ≤ 𝜌 < 0,8 strong correlation/monotonic relationship.
• 0,8 ≤ 𝜌 < 1 very strong correlation/monotonic relationship.
• 𝜌 = 1 perfect correlation/monotonic relationship.
For 𝑛 > 30, the value of z has to be calculated first. The formula to calculate z is
𝑧 = 𝜌√𝑛 − 1
(3)where the value of 𝜌 is calculated with the previous formula – formula (1) or (2), depends whether the data has tied ranks. After the value of z is obtained, it has to be compared to the value of z from the table of Normal Curve Areas to obtain critical values. The Normal Curve Areas table can be seen in statistical books.
In Spearman’s rank correlation test, the analyzed variables don’t have to be normally distributed. Thus, if the variables are not normally distributed, Spearman’s rank correlation test still can be used to analyze the correlation or the monotonic relationship between those variables. Moreover, in Spearman’s rank correlation test the analyzed variables have to have neither linear nor even monotonic relationships. If the researcher is not certain whether the analyzed variables have a linear or monotonic relationship, he is still allowed to use Spearman’s rank correlation test.
Spearman’s rank correlation test has been used in previous research to analyze the relationship between independent and dependent variables. Rosdiana, Tjeng, and Sudarso [10] used Spearman’s rank correlation test to analyze the relationship between leukocyte, platelet count, hematocrit, and clinical degree of dengue. Nurminha, Sugiarti, and Aulia [8] used Spearman’s rank correlation test to analyze the relationship between albumin and clinical degree of dengue. While Rasyada, Nasrul, and Edward [9] used Spearman’s rank correlation test to analyze the relationship between hematocrit and platelet count.
In this research, Spearman’s rank correlation test is used to analyze the correlation between the laboratory characteristics on the third day of observation – mentioned in the first chapter – and the clinical degree of dengue. In this research, correlation is defined as an impact where the laboratory characteristics on the third day of observation affect the clinical degree of dengue in the critical phase. If there is a correlation between them – in another word, impact, the robustness of the impact of those laboratory characteristics on the clinical degree of dengue in the critical phase will be analyzed.
RESULTS
In this research, the relationship between the independent variables (x) and the dependent variable (y) of dengue patients’ data of the year 2009 will be analyzed. Nevertheless, variables used in this research are only variables on the third day of observation, which is variables from the patients’ data on the third day of being hospitalized. It is because on the third day, dengue patients are entering the critical phase (critical phase of dengue occurs on day fourth to sixth).
Those variables are only analyzed when the patients are entering the critical phase because results obtained from this research will be used in the development of Machine Learning predictor program which will be able to predict the clinical degree of dengue in the critical phase, if the laboratory characteristics are known.
Independent variables (x) are defined as laboratory characteristics of dengue patients. Those laboratory characteristics consist of Fibrinogen, Hemoglobin, Hematocrit, Leukocyte, Thrombocyte (Platelet Count), Neutrophil, Lymphocyte, and Monocyte. While dependent variable (y) is defined as the diagnosis obtained by the patients based on their laboratory characteristics results. Variable y is classified into three different clinical degrees according to its
severity level, which is DF (Dengue Fever) as the mild level, DHF (Dengue Hemorrhagic Fever) grade 1 as the intermediate level, and DHF grade 2 as the severe level.
This research is conducted in three steps. Below is the explanation of those steps:
1. Finding the missing values.
In dengue patients’ data 2009, the values of Neutrophil, Lymphocyte, and Monocyte on the third day of observation are empty. Those empty values are called missing values. The first step that has to be conducted in this research is finding the missing values.
2. Finding find laboratory characteristics that affect the clinical degree of dengue in the critical phase.
As explained previously, independent variables (x) in this research consist of eight laboratory characteristics.
Let each of those laboratory characteristics stated as follows:
x1 : Fibrinogen x2 : Hemoglobin x3 : Hematocrit x4 : Leukocyte
x5 : Thrombocyte (Platelet Count) x6 : Neutrophil
x7 : Lymphocyte x8 : Monocyte
This step is conducted by analyzing each of the x variables individually and observing whether each of them has a relationship or correlation with variable y. Analysis is conducted using Spearman’s rank correlation test.
First, a two-ways hypothesis test is done, where the hypotheses are as follows:
H0: 𝜌 = 0, which means there is no significant correlation between variable x and y. Furthermore, variable x doesn’t affect variable y.
H1: 𝜌 ≠ 0, which means there is a significant correlation between variable x and y. Furthermore, variable x affects variable y.
To accept or reject H0, there is a p-value (Sig. 2-tailed) in hypothesis test, which the interpretation is as follows:
a. Level 0.05
If p-value < 0.05, reject H0. There is a significant correlation between variable x and variable y.
Furthermore, variable x affects variable y.
If p-value > 0.05, accept H0. There is no significant correlation between variable x and variable y.
Furthermore, variable x doesn’t affect variable y.
b. Level 0.01
If p-value < 0.01, reject H0. There is a significant correlation between variable x and variable y.
Furthermore, variable x affects variable y.
If p-value > 0.01, accept H0. There is no significant correlation between variable x and variable y.
Furthermore, variable x doesn’t affect variable y.
The level used in this two-ways hypothesis test is 0.05.
As explained previously, this step is conducted by analyzing each of the x variables one by one and observing whether each of them has a relationship or correlation with variable y or not. For example, it will be analyzed whether variable x1 on the third day of observation affects the diagnosis obtained by the patients. Thus, the hypotheses are as follows:
H0: 𝜌 = 0, which means there is no significant correlation between variable x1 on the third day of observation and variable Diagnosis. Furthermore, on the third day of observation, x1 doesn’t affect the diagnosis obtained by the patients.
H1: 𝜌 ≠ 0, which means there is a significant correlation between variable x1 on the third day of observation and variable Diagnosis. Furthermore, on the third day of observation, x1 affects the diagnosis obtained by the patients.
For example, the obtained p-value is 0.025. It implies that H0 is rejected. Thus, there is a significant correlation between variable x1 on the third day of observation and variable Diagnosis. Furthermore, on the third day of observation, x1 affects the diagnosis obtained by the patients.
3. Analyzing how robust the impact of those laboratory characteristics on the clinical degree of dengue in the critical phase.
This step is conducted by interpreting the 𝜌 value obtained by performing Spearman’s rank correlation test.
Continuing the example from point 2, for example, the obtained 𝜌 value is 0.085. It implies that the
correlation/monotonic relationship between variable x1 on the third day of observation and variable Diagnosis is very strong.
By conducting the three steps explained above, below are the results obtained from this research:
1. Fibrinogen
TABLE 1. The result of Spearman’s rank correlation test between Fibrinogen on the third day of observation and Diagnosis.
Fibrinogen Diagnosis
𝜌 Fibrinogen Correlation Coefficient 1 0.25
Sig. (2-tailed) 0 0.068
N 54 54
Diagnosis Correlation Coefficient 0.25 1
Sig. (2-tailed) 0.068 0
N 54 54
There is no significant correlation between variable Fibrinogen on the third day of observation and variable Diagnosis. Furthermore, on the third day of observation, Fibrinogen doesn’t affect the diagnosis obtained by the patients.
2. Hemoglobin
TABLE 2. The result of Spearman’s rank correlation test between Hemoglobin on the third day of observation and Diagnosis.
Hemoglobin Diagnosis
𝜌 Hemoglobin Correlation Coefficient 1 0.01
Sig. (2-tailed) 0 0.943
N 54 54
Diagnosis Correlation Coefficient 0.01 1
Sig. (2-tailed) 0.943 0
N 54 54
There is no significant correlation between variable Hemoglobin on the third day of observation and variable Diagnosis. Furthermore, on the third day of observation, Hemoglobin doesn’t affect the diagnosis obtained by the patients.
3. Hematocrit
TABLE 3. The result of Spearman’s rank correlation test between Hematocrit on the third day of observation and Diagnosis.
Hematocrit Diagnosis
𝜌 Hematocrit Correlation Coefficient 1 -0.007
Sig. (2-tailed) 0 0.96
N 54 54
Diagnosis Correlation Coefficient -0.007 1
Sig. (2-tailed) 0.96 0
N 54 54
There is no significant correlation between variable Hematocrit on the third day of observation and variable Diagnosis. Furthermore, on the third day of observation, Hematocrit doesn’t affect the diagnosis obtained by the patients.
4. Leukocyte
TABLE 4. The result of Spearman’s rank correlation test between Leukocyte on the third day of observation and Diagnosis.
Leukocyte Diagnosis
𝜌 Leukocyte Correlation Coefficient 1 -0.119
Sig. (2-tailed) 0 0.392
N 54 54
Diagnosis Correlation Coefficient -0.119 1
Sig. (2-tailed) 0.392 0
N 54 54
There is no significant correlation between variable Leukocyte on the third day of observation and variable Diagnosis. Furthermore, on the third day of observation, Leukocyte doesn’t affect the diagnosis obtained by the patients.
5. Thrombocyte (Platelet Count)
TABLE 5. The result of Spearman’s rank correlation test between Platelet Count on the third day of observation and Diagnosis.
Platelet Count Diagnosis
𝜌 Platelet
Count
Correlation Coefficient 1 -0.04
Sig. (2-tailed) 0 0.773
N 54 54
Diagnosis Correlation Coefficient -0.04 1
Sig. (2-tailed) 0.773 0
N 54 54
There is no significant correlation between variable Platelet Count on the third day of observation and variable Diagnosis. Furthermore, on the third day of observation, Platelet Count doesn’t affect the diagnosis obtained by the patients.
6. Neutrophil
TABLE 6. The result of Spearman’s rank correlation test between Neutrophil on the third day of observation and Diagnosis.
Neutrophil Diagnosis
𝜌 Neutrophil Correlation Coefficient 1 -0.025
Sig. (2-tailed) 0 0.857
N 54 54
Diagnosis Correlation Coefficient -0.025 1
Sig. (2-tailed) 0.857 0
N 54 54
There is no significant correlation between variable Neutrophil on the third day of observation and variable Diagnosis. Furthermore, on the third day of observation, Neutrophil doesn’t affect the diagnosis obtained by the patients.
7. Lymphocyte
TABLE 7. The result of Spearman’s rank correlation test between Lymphocyte on the third day of observation and Diagnosis.
Lymphocyte Diagnosis
𝜌 Lymphocyte Correlation Coefficient 1 0.072
Sig. (2-tailed) 0 0.604
N 54 54
Diagnosis Correlation Coefficient 0.072 1
Sig. (2-tailed) 0.604 0
N 54 54
There is no significant correlation between variable Lymphocyte on the third day of observation and variable Diagnosis. Furthermore, on the third day of observation, Lymphocyte doesn’t affect the diagnosis obtained by the patients.
8. Monocyte
TABLE 8. The result of Spearman’s rank correlation test between Monocyte on the third day of observation and Diagnosis.
Monocyte Diagnosis
𝜌 Monocyte Correlation Coefficient 1 0.029
Sig. (2-tailed) 0 0.836
N 54 54
Diagnosis Correlation Coefficient 0.029 1
Sig. (2-tailed) 0.836 0
N 54 54
There is no significant correlation between variable Monocyte on the third day of observation and variable Diagnosis. Furthermore, on the third day of observation, Monocyte doesn’t affect the diagnosis obtained by the patients.
CONCLUSION AND RECOMMENDATIONS
If measured individually, all of the laboratory characteristics on the third day of observation that are analyzed in this research don’t affect the clinical degree of dengue in the critical phase. Furthermore, if measured individually, all of the laboratory characteristics that are analyzed in this research will not be used as predictors of dengue for the Machine Learning predictor program.
There are some limitations in this research. It would be much appreciated if other researchers that will conduct further research about dengue consider these recommendations below:
1. Laboratory characteristics that are analyzed in this research are very limited. In further research, it would be better to analyze other laboratory characteristics that haven’t been analyzed in this research, such as Immunoglobulin G, Immunoglobulin M, etcetera.
2. Data used in this research is only data on the third day of observation, which is data of the patients on the third day of being hospitalized. In further research, it would be better to analyze data on the other days of observation, particularly on day fourth to sixth. Because on those days, dengue patients are in the critical phase.
3. In this research, the eight laboratory characteristics were analyzed individually, whether each of them has a relationship or correlation with the clinical degree of dengue. In further research, the laboratory characteristics can be analyzed simultaneously due to the possibility of different results.
4. Data used in this research only consists of 54 data of dengue patients as the samples. In further research, it would be better to use data which consists of a larger number of samples, so the data will better represent the population of dengue patients in the analyzed region.
Correlation test that is used in this research is Spearman’s rank correlation test. In further research, other correlation tests can be used to analyze the correlation between the laboratory characteristics and clinical degree of dengue, such as Pearson correlation test. Researchers who conduct further research about dengue can observe the results obtained using other correlation tests. Do other tests yield the same results as the ones obtained using Spearman’s rank correlation test in this research? If the results are different, it would be better to further analyze which results are better, or if possible, the best. The better results should be the ones used in the development of Machine Learning predictor program which will be able to predict the clinical degree of dengue in the critical phase, if the laboratory characteristics are known.
REFERENCES
1. Andriyoko, B., et al. MKB, 44(4), 253-260 (2012).
2. Anggraeni, W., et al. Procedia Computer Science, 124, 142-150 (2017).
3. Candra, A. Aspirator, 2 (2), 110-119 (2010).
4. Götz, T., et al. Ecological Complexity, 30, 57-62 (2016).
5. Gulati, S., dan Maheshwari, A. Trop Med Int Health, 12(9), 1087-1095 (2007).
6. Hidayati, L., Hadi, U. K., & Soviana, S., Acta Veterenaria Indonesiana, 5(1), 22-28 (2017).
7. Lardo, S., et al., Asian Pacific Journal of Tropical Medicine, 9(2), 134-140 (2016).
8. Nurminha, Sugiarti, M., & Aulia, M. G., Jurnal Analis Kesehatan, 7(2), 717-723 (2018).
9. Rasyada, A., Nasrul, E., & Edward, Z, Jurnal Kesehatan Andalas, 3(3), 343–347, (2014).
10. Rosdiana, Tjeng, W. S., & Sudarso, S., Sari Pediatri, 19(1), 41-45 (2017).
11. Suryani, E. T., Jurnal Berkala Epidemiologi, 6(3), 260-267 (2018).
