Valuable Health Insights Using Effective Predicting Models For Diabetic Patients

Abstract

This research paper proposes a novel approach to personalized health monitoring systems using IT solutions and machine learning models to provide regional language prescriptions and an effective way of health monitoring for diabetes patients. We focus on the effective use of existing predicting models to provide valuable health insights and explore the benefits of using a logistic regression model for predicting diabetes status. Our approach involves not only classifying the diabetes status of a patient but also providing personalized dietary and exercise recommendations based on their readings. The function defined in our study automates this process and provides tailored recommendations for each patient. Our experimental results demonstrate that the logistic regression model performs well in predicting diabetes status and our approach provides useful recommendations for patients. This research contributes to the growing body of literature on the use of machine learning in healthcare and highlights the importance of personalized healthcare in improving patients' overall well-being.