Prediction Of Diabetes Using Machine Learning Algorithms

Abstract

Diabetes is a frequent condition in humans that is brought on by a collection of metabolic diseases in which the body's sugar levels remain abnormally high for an extended length of time. Because it damages many of the body's systems by affecting various organs, we are all trying to prevent diabetes at an early stage by anticipating its symptoms using a variety of techniques. Human life can be saved by controlling such diseases early on. In order to accomplish the goal, this research project primarily uses machine learning approaches to investigate differentrisk variables associated with this disease. Effective knowledge extraction is accomplished by machine learning methods that build prediction models using diagnostic medical datasets from diabetic patients. It may be possible to forecast diabetic people by gleaning information from such data. K-Means Cluster, Naive Bayes (NB), Support Vector Machine (SVM), K- Nearest Neighbor (KNN), Linear Regression, Decision Tree (DT), Logistic Regression, Random Forest (RF) and Hierarchical Cluster are nine well-known machine learning algorithms that I use in this work to predict diabetic disease using data from the adult population. In comparison to other machine learning methods, the findings from my experiments indicate that when compared to alternative approaches, the C4.5 decision tree attained a greater level of accuracy.