Enhancing Early Diagnosis: Machine Learning Applications in Diabetes Prediction

Abstract

Diabetes is a persistent illness that affects a huge number of individuals around the world. Early diagnosis and treatment of diabetes is critical for forestalling difficulties and further developing well-being results. Machine learning procedures offer promising answers for upgrading early diabetes expectations and determination. Based on a variety of data sources, this paper examines the recent machine-learning applications for diabetes prediction. The findings demonstrate that diabetes onset and risk can be accurately predicted using machine learning models applied to biomedical data, wearable device data, and electronic health records. For instance, random forest models utilising fasting plasma glucose, BMI, and age gave 93% precision in diabetes expectations. Profound brain networks utilising genomic and stomach microbiome information achieved 89% exactness. Machine learning techniques show excellent performance for diabetes prediction across diverse data types. Challenges remain in model interpretability and integration into clinical workflows. Further research on predictive feature selection, model optimisation, and clinical implementation will enable enhanced early diabetes diagnosis through machine learning. With accurate and early prediction, patients can receive prompt treatment to manage diabetes progression better.