Early-Stage Diabetes Risk Prediction Using Supervised Machine Learning Algorithms

Abstract

Diabetes is a common health problem worldwide; it is especially pervasive in Bangladesh. The condition manifests in a person when his blood sugar is consistently high. It also contributes to other health problems like blindness, renal failure, heart attack, and stroke. If you know about the early stage, you can take charge and maybe save someone's life. Sadly, this illness is spreading rapidly. The purpose of this research was to quantitatively evaluate the effectiveness of many widely used Machine Learning methods. The medical field is only one area that has benefited greatly from recent advancements in Machine Learning technology. Machine learning algorithms come in a wide variety. Nevertheless, in this research we employ five well-known machine learning algorithms to determine performance metrics: Gaussian Naive Bayes, Random Forest, Support Vector Machine, Logistic Regression, and the Decision Tree classifier. Using real data from diabetic patients in Bangladesh, these algorithms were developed and evaluated. There are 3837 patient records in the dataset, 3057 of which correspond to affected cases and 396 were normal. Out of 5 different machine learning algorithms, Random Forest achieved the highest 98% accuracy.