Predicting Microvascular Complications in Diabetic Mellitus Using Improved Enhanced Coati Optimizer

Abstract

Objective: 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. Method: In this paper, a machine learning model is proposed for predicting and detecting microvascular diseases in type 2 diabetic Patients. The initial stage is preprocessing where data processing . After the preprocessing operation is performed feature selection process is carried out using the Improved Enhanced Coati algorithm. The optimal features from the Improved Enhanced Coati Optimization algorithm are applied to various classification algorithms. The reason behind applying this feature selection algorithm to various models is to check the performance with the traditional classifiers. Hence model performance is compared with XGB, KNN, SVM, RF, AdaBoost, Tree, and ANN algorithms. Findings: For the classification of diabetic retinopathy, the selected features are age, sex, BMI, BP, FPS, Family History, and Medical Adherence. Similarly, the features selected to classify Diabetic Nephropathy as 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 algorithms as XGB, KNN, SVM, RF, AdaBoost, Tree, and ANN. The results are measured by considering parameters for training and testing 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, and diabetic neuropathy is 99.8% and 95.44%. Novelty: In the proposed methodology the feature selecting fitness function is selected based upon the received optimal accuracy from the feature selecting estimator as AdaBoost. In Coati Optimizer the feature selection process is carried out by selecting a fitness function that provides the minimum error.