A Comparative Analysis Of Logistic Regression and K-Nearest Neighbors Algorithms In Diagnosis Of Diabetes

Abstract

Machine Learning techniques have gained prominence in medical diagnosis due to their ability to uncover patterns in complex data-sets, thereby giving accurate disease classification. In this study, we mainly focus on the application of two widely used Machine Learning algorithms, Logistic Regression and K-Nearestneighbors( KNN), for the purpose of distinguishing patients with diabetes from those without. Our research aims to shed light on the comparative accuracy and performance of these algorithms in a medical context. The methodology section outlines experimental setup, detailing data processing, algorithm training and testing procedures. A comprehensive data-set comprising medical attributes is utilized for evaluation and accuracy metrics are employed to quantify the performance of the algorithms. Results has shown efficacy of both the algorithms and our findings showcase the strengths and limitations of each approach, contributing on the applicability in medical decision making. By offering a nuanced comparison, we illuminate a path for more robust and accurate disease identification techniques, further enhancing patient care and medical outcomes.