Predicting Microvascular Complications in Diabetic Mellitus Using Improved Enhanced Coati Optimizer

Abstract

Diabetes complications are classified as Macro and Microvascular Diseases. Microvascular complications in type 2 Diabetic patients commonly occur as diabetic retinopathy, diabetic neuropathy, and diabetic nephropathy. Therefore detecting these microvascular complications from the clinical dataset is very important. The paper proposed a machine learning model for predicting and detecting microvascular diseases in type 2 diabetic Patients. In the initial stage data preprocessing is performed upon data. After the preprocessing feature selection is done using the Improved Enhanced Coati algorithm. The optimal features from the Improved Enhanced Coati Optimization algorithm are applied to various classification algorithms. The results are obtained for traditional classifiers such as XGB, KNN, SVM, RF, AdaBoost, Tree, and ANN algorithms. For the classification of diabetic retinopathy, the selected features are age, sex, BMI, BP, FPS, Family History, and Medical Adherence. Similarly, the features used to classify Diabetic Nephropathy are Sex, SP, FPS, Family History, Onset Age, and HbA1C and FPS used to classify Diabetic Neuropathy. On optimal selection of features various ML classification algorithms are applied. The results are compared with XGB, KNN, SVM, RF, AdaBoost, Tree, and ANN. The results are measured for training and testing on parameter accuracy and Random Forest Classifier results are optimal for the AdaBoost estimator for type 2 diabetic patients for the diabetic retinopathy is 99.9% and 94.78%, diabetic nephropathy, diabetic neuropathy is 99.8% and 95.44%. In the proposed methodology the feature-selecting fitness function is selected based on the received optimal accuracy from the feature-selecting estimator as AdaBoost. In Coati Optimizer the feature selection process is done by a fitness function that provides the minimum error.