Predictive Modeling for Diabetes: A Machine Learning Approach

Abstract

Millions of individuals across the globe are suffering from diabetics, a chronic metabolic disease that, if left untreated or improperly managed, can cause serious health problems. Diabetes may be brought on by a person's age, weight, lack of exercise, genetic diabetes, lifestyle choices, Unhealthy eating habits, elevated blood pressure, etc. Individuals with diabetes may face severe complications, including cardiovascular issues, Renal issues, hypertension problems, vision damage, and other organ problems. If diabetes is identified early, It is manageable in paper, we advocate for a diabetes prediction model to enhance diabetes classification that applies diverse machine learning approaches, for demonstration Vector based learning models , Decision Trees, and Logistic Regression, and neuro- inspired algorithms, and includes several externalelements that contribute which focus on identifying standard elements like Glucose, Age, Insulin, and so on. We have the goalto give the best, efficient approach for predicting diabetes by contrasting the sensitivity and precision of each model. An intuitive, data-driven tool for evaluating diabetes risk, enhancing patient care, and lessening the strain on the healthcare system may be made available to healthcare practitioners by this initiative. To improve forecast accuracy, future research can involve adding more data and health variables to the model.