AntLion Optimization Algorithm based Type II Diabetes Mellitus Prediction

Abstract

Diabetes Mellitus is one of the common diseases prevailing in most developed and developing countries. In recent decades, there has been a huge rise in diabetes patients in India. Based on recent statistics, nearly 72.96 million young people are suffering from diabetes. Thus, it is essential to diagnose diabetes at an early stage. In this work, the PIMA dataset is used to design an optimized and super-vised learning model based on K-nearest neighbor classification. The optimization algorithm used to generate useful features to predict diabetes mellitus is the Antlion optimization algorithm. The proposed work yields an accuracy of 80% for the selected features like Pregnancy, BMI, BP, Age, Glucose, and Diabetes Pedigree Function.