Machine Learning For Early Diabetes Detection And Diagnosis With KNN

Abstract

Diabetes mellitus is a hastily developing international health problem that necessitates early detection and effective management to prevent severe complications. This study leverages machine learning, specifically the K-Nearest Neighbors algorithm, to predict and diagnose diabetes at an early stage. By analyzing a diverse dataset that includes biological, sociological, and clinical features, the studies goals to develop a robust predictive model. The application of KNN, alongside other machine learning techniques, permits for the advent of tools that can assess individual risk, enabling personalized remedy plans and optimizing healthcare management. The findings of this research could appreciably decorate early diabetes detection, leading to better patient outcomes and contributing to the fight against the diabetes epidemic. This study underscores the the capacity of machine gaining knowledge in transforming public health strategies and providing actionable insights for healthcare practitioners.