Predictive Machine Learning Models for Early Diabetes Diagnosis: Enhancing Accuracy and Privacy with Federated Learning

Abstract

Millions of people in the world are affected by diabetes, which is a serious chronic illness that have need of early detection for effective management and treatment. Even if they work well, typical techniques for detection are normally very expensive, time-consuming, and invasive. In this regard, machine learning (ML) has become a ground-breaking method for diabetes detection, providing an exact, effective, and non-invasive substitute. We are using the 27,690 instances and nine attributes of the Kaggle diabetes dataset, a combined machine learning model is presented in this paper. We utilized three machine learning algorithms: XG Boost (XGB), Naïve Bayes (NB), and K-Nearest Neighbours (KNN). XGB had the finest accuracy, coming in at 90%. To improve model performance while defending data confidentiality, our methodology includes data collection, pre-processing, training, testing, and parameter adjustment inside a united learning framework. The findings validate machine learning's marvellous potential for enhancing diabetes diagnosis, simplifying early intervention, and lowering medical expenses. Federated learning's integration further keeps patient privacy and data safety, giving it a solid option for extensive clinical use. This work opens the door for more accurate, effective, and accessible healthcare resolutions by highlighting the crucial implication and effectiveness of ML based diabetes prediction.