BREAKING BARRIERS IN DIABETES MANAGEMENT: UNLEASHING THE POTENTIAL OF MACHINE LEARNING IN DIAGNOSES AND TREATMENT

Abstract

The public's health is seriously threatened by diabetes, a lifelong metabolic disorder that affects people all over the world. Diabetic patients require innovative approaches to care as conventional methods struggle to diagnose them precisely and tailor treatments to each individual. It would be wonderful if a few straightforward tests could determine if someone has diabetes. With an easy, quick test, individuals might be able to receive a diagnosis earlier, enabling them to make healthy lifestyle adjustments and reducing their likelihood of contracting new illnesses. A ML-based approach to diabetes management can reduce barriers to diagnosis and treatment by reducing diagnostic and treatment errors. Machine learning algorithms offer unprecedented proficiency when it comes to predicting diabetes and identifying it based on immense volumes of patient data. By using sophisticated data processing tools, we analyze a variety of variables, such as genetic predisposition, lifestyle choices, and clinical signs. High-risk individuals can be identified using ML models in order to prevent diabetes. The condition threatens a person's life because it can damage the heart, kidneys, and nerves. Further, ML-based diagnostic techniques can assist in identifying and treating various diabetes subtypes, improving treatment effectiveness. ML-driven therapy algorithms optimise insulin doses, food suggestions, and exercise routines by taking into account individual variances and dynamically responding to changing conditions, Consequently, they are less likely to suffer complications and their blood sugar levels are improved. In addition, ML-based decision support systems provide real-time insights and suggestions to healthcare providers, enabling proactive and knowledgeable therapeutic treatments.