Stochastic Learning Strategy with Varying Regularization Activation Layer-Based Diabetes Prediction Using Multilayer Perceptron

Diabetes makes people more susceptible to disorders like heart disease, kidney disease, Parkinson’s, cataracts, respiratory issues, and other illnesses. Numerous tests are employed to capture essential data for diagnosing diabetes, and the assessment is then used to decide the most appropriate course of action. Nevertheless, it is still challenging to identify the precise traits essential for the emergence of diabetes. Finding the critical factors that influence the target diabetes condition is the primary goal of this research. The Stochastic Learning Strategy with Varying Regularization Logistic Activation Multilayer Perceptron (MLP) model has been implemented with the Indian Diabetes patient dataset extracted from the UCI database repository. Data preprocessing was accomplished for the patients of the Indian diabetes dataset. This research tries to extract the correlation and distribution of all features from the Indian Diabetes Patient dataset. All classifiers demonstrate an accuracy of less than 80% when applied to the Indian Diabetes patient dataset with and without feature scaling, except the Random Forest classifier, which is found to have an accuracy of 78%, which is not the best. The Indian Diabetes patient dataset is fitted with MLP with various learning rates such as Constant learning, Constant momentum and Constant Nesterov momentum learning rate, Invasive Scaling, Invasive Scaling momentum and Invasive Scaling Nesterov momentum learning rate, Adaptive learning, Adaptive momentum and Adaptive Nesterov momentum learning rate for various activation layers like ReLU (Rectified Linear Unit), Identity, tanh and logistic activation layer and the performance is analyzed. The MLP with a logistic activation layer under the Adaptive momentum Learning rate strategy is examined with varying regularization parameter alpha to strengthen the prediction accuracy. The proposed Stochastic Learning Strategy with Varying Regularization Logistic Activation MLP model was further analyzed with the performance metrics precision, recall, FScore, runtime and accuracy and the experimental results exhibit 97% accuracy for MLP with logistic activation layer under Adaptive momentum Learning rate toward predicting the diabetes disease.