To develop an expert system on mobile platform to provide user with a system capable of diagnosing the user's disease based on the symptoms specified. This project will cover the research part and based on the findings of the research, the expert system will be developed. A physician can have knowledge of most diseases, but due to the large number of diseases, a physician can benefit from the support provided by an expert system to quickly isolate the disease.
From the description, we can understand that an expert system is necessary when it comes to accuracy, cost effectiveness and reliability. An expert system, also known as a knowledge-based system, is a computer program that contains the knowledge and analytical skills of human experts related to a specific topic. On the other hand, an expert system is a software system that incorporates concepts learned from experts in a particular field and uses their knowledge to provide problem analysis to software users.
An expert system differs from more conventional applications in that it simulates human reasoning about a problem domain, rather than simulating the domain itself. BIMES works like other expert systems that start by asking the user's type of illness and then display a list of symptoms for the user to choose from.
METHODOLOGY
- Project Flow Chart
- Research Methodology
- System Development
- Project Activities
- Key Milestone
- Tool
- Software
- Hardware
- Diseases and Symptoms Identification
- System Architecture
To implement the system, suitable software has been identified for developing the system on mobile platform, which is App Inventor. After that, result validation and verification is done to ensure that the output produced is accurate and precise according to the planning. Finally, the source code of the system will be uploaded to the web hosting server for public viewing to fulfill the purpose.
This led to the second phase where an appropriate questionnaire was designed and used for a usability test study conducted in the University Technology PETRONAS. The study helps to identify the problems and effectiveness of the Islamic Medicine Expert System. In the fourth step, the system design will be upgraded to improve the current system.
Where system development consists of four (4) phases, these are planning, analysis, design and evaluation. The fourth phase is evaluation, where the system is evaluated by the Islamic medication expert. It will be used in this project to create the application banner or any other image that will be inserted into the application system.
The Android smartphone is used to test the application and the workstation is used to develop the system. Eyes become red when you exert yourself or have an excessive cough TRUE Eyes produce more water than normal (Drainage) TRUE TRUE. Bloating or fullness after just a few bites of food TRUE TRUE Burning sensation in the chest (chest pain, tightness in the chest, tightness in the chest) TRUE.
RESULTS AND DISCUSSION
Survey for Islamic Medication Expert System
Analysis: From the result, it appears that 36.4% of the respondents find it with good user interface and 9.1% of the respondents rate it as poor interface. This is evidence that the system is beneficial to people and beneficial to their lives. Analysis: From the result, 90.9% of users will install the app on their phone if BIMES is built in Android app.
Dataset Gathering
Drainage Vision problems Eye pain Flashing Vision problems Eye pain Vision problems Redness Eye pain. From the data set above, we can learn about a possible combination of two symptoms that can cause a disease. The above data set is obtained by consulting medical professionals based on an actual case where a patient is likely to have any two possible symptoms while experiencing the disease.
From the data set, we can understand which symptoms are greater or lesser in certain diseases. For example, when a patient has red eyes, the main symptom is redness in the eye. It is highly unlikely that a patient with eye redness does not have symptoms of eye redness.
Therefore, when we produce the eye redness dataset, for example, the redness in the eye symptom.
Diagnosis Result
Based on the user input, the system will diagnose the likelihood that the user will experience certain diseases. The calculation shows that the chance that the user will suffer from burning and red eyes is 0.005 and 0.042 respectively. So we can say that the user is most likely suffering from eye redness and the system will suggest the treatment for eye redness.
Based on the calculation, we got 0.000 probability for the user expected to experience eye pain disease. This is because eye damage is not one of the symptoms of eye pain as it is not found in the dataset. The theorem is that it eliminates the disease that is not related to the symptoms specified by the user.
Percentage Calculation
By calculating in percentages, we can know that the probability that the user will experience burning eyes and red eyes is 10.6% and 89.3% respectively. From the percentage, the user will know that he most likely has red eyes. With this information, the user can refer to the suggested Islamic treatment provided by the system or consult a doctor.
System Interface
The user has to select any disease tab and tick the possible symptoms that may occur with a particular disease. The symptoms of a particular disease will appear on the screen and the user has to tick the checkbox for the corresponding symptoms. The result will be displayed in the form of percentages, which shows the possibility of the disease that the user had.
The user can click on the Treatment button to review the Islamic treatment of the disease in question. Below is the interface when the system has calculated the percentage the user is likely to have certain diseases. The reciter of the Quran or Hadith appears on the screen to cure the particular disease.
The instruction of body movements or explanation of the verses will also appear on the screen. If the user has any questions about the system user, he can click the About Us button. The contact person appears. If the user wants to close the application, the Close Application button at the bottom of the screen is used.
After the user selects their disease symptoms, the system will analyze the combination symptoms and calculate them in the Bayes theorem to output the possible disease that the user is facing. The result will appear at the bottom of the screen in types of diseases selected by the user.
The Comparison between Bayes’ Theorem and Rule-Based
From Table 9, we can determine the percentage of users who have certain diseases using Bayes' Theorem and Rule Based:. Based on Test 1, both algorithms accurately diagnose the user's disease with both accounting for chickenpox 100%. From Table 10, we can determine the percentage of users who have certain diseases using Bayes' theorem and Rule Based:.
Analysis: From test 2, the rule-based system gives more accurate results because it calculates the highest percentage that the user is expected to experience is chicken pox with 42% being true on the other hand the Bayesian system only calculates the percentage of having sheep to be 27. %. But we have to keep in mind that the user had given a small symptom of chicken pox which is fever. Although the user provided only one symptom, the rule-based system calculated different percentages for each disease.
In this test, we assume that the user has no disease and simply provides two unrelated symptoms to the system. Analysis: From test 3, the Bayesian system will not provide any result to the user because the combination of symptoms itchy rash and runny nose does not appear in the chickenpox, flu/flu, seizure and yellow fever dataset. In this test, we assume that the user has no disease and simply ticks all symptoms.
Using the Bayesian theorem, the system will eliminate any irrelevant diseases based on the combination of symptoms provided by the user. At the same time, the system can detect if the user simply ticks any symptoms and returns null results to the user to warn that there are no such combinations from the data set. But if the combination of symptoms provided by the user is not found in the dataset, the strictly Bayesian system does not return any value to the user.
In this case, the rule-based system can anticipate the disease that the user is likely to have.
CONCLUSION AND RECOMMENDATION
Conclusion
The concept of Bayes theorem has been properly implemented in the diagnosis process and the result produced by the system is satisfactory. However, there are some tips to increase the combination of symptoms and also the number of diseases. In addition, according to the previous report, Bayes theorem may not give the most accurate result compared to other algorithms.
How can we ascertain the most likely causes of observed diseases given a variety of evidence or symptoms. The second task is making treatment decisions: What can we do to treat the problem. In a medical application, hypotheses are possible diseases, and findings may include the patient's history and symptoms.
Despite the success of this simple Bayesian scheme in several of these medical applications, there are some limitations. One reason could be the general lack of attention to integrating systems with the diagnostic practitioner's habits and environment. A second and more frequently cited reason is the restrictiveness of the assumptions of mutual exclusivity and conditional independence.
More generally, critics have pointed out the limited expressiveness of this formulation and the apparent mismatch between the rigorous, formal, quantitative approach to probabilistic inference and the informal, qualitative nature of human reasoning. This mismatch leads to difficulties in encoding expertise and explaining results so that users can understand and trust them (Davis 1982; Gorry 1973; Szolovits 1982).
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