Spectrum-Related Neurological Disorder Using Machine Learning
Model 3: Logistic Regression The results obtained were as follows
Mean=76.16%, median=75.16%, mode=75.15%, accuracy=99.16%
Model 4:K-Nearest Neighbors
The dataset was fitted onto the model with different sizes of number of neighbors each time to compare how many neighbors need to be considered to get the most accurate classification. The accuracy calculated through the following class label formulates (Tables2.2and2.3):
All models will be utilized with testing, time, accuracy of the dataset, the accura- cies ranging from 68 to 99%. The key features across these four models were kept the same. The features include the feature scores obtained for the patients, gender of the child, ethnicity, family history of ASD, and age (in months).
This research work used a total of 705 data entries of various patients with their respective class as ASD status (autistic or not). All the data entries are kept in csv format and used for this study (Tables2.4and2.5).
The accuracy of the applied models has been studied and compared with each other, and the estimated accuracy comparison is listed in Fig.2.1. The comparative chart shown in Fig.2.1displays the logistic regression-based model that estimated highest accuracy of 99.16%, which is maximum among all.
Table 2.3 Model accuracy
formula Predicted class
C1 C2
Actual class C1 True positives False negatives C2 False positives True negatives
2 Statistical Approach to Detect Alzheimer’s Disease … 23 Table 2.4 Comparison of
model accuracy Algorithm Accuracy (in percentage)
Multiple linear regression 68.65 Polynomial regression 96.25 Logistic regression 99.16
KNN 97.57
Table 2.5 Model accuracy of
KNN with varyingK values K value Accuracy (in percentage)
3 97.57
5 96.10
7 97.27
9 97.10
0 20 40 60 80 100 120
Multiple Linear Regression
Polynomial Regression
KNN Logistic regression
Accuracy
Multiple Linear Regression Polynomial Regression
KNN Logistic regression
Model ->
Accuracy ->
Fig. 2.1 Accuracy for various models
2.5 Conclusion
It was observed that the classification model gave more accurate results than the regression models for both the given datasets. Classification models should be pre- ferred. Also, for our datasets, it was also noticed that filling the missing values with mean values gave the highest accuracy on selected models. Polynomial regression also gave decently accurate predictions; however, polynomial regression is very prone to overfitting; thus, it is not preferred for such kind of classification problems. There- fore, for both the given datasets (detection of Alzheimer’s disease and detection of autism in patients), logistic regression is the best model for classification.
24 A. K. Sharma and D. K. Shrivastav
2.6 Future Work
This research work discusses machine learning in the medical domain is helping the world to innovate and help society in many ways. Its contribution to the medical society can continue to save many lives. It can be used to predict many other diseases related to the brain, the heart, etc., which require laboratory tests. Data can be col- lected directly from research laboratories to improve the consistency of prediction.
Pictorial data can also be used to directly predict results from MRI scans, EEG scans, ultrasound scans, as well as ECG scans. The IoT devices can be used to capture the real-time health statistics and can be used to predict and to perform real-time alerts systems as well.
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