Polymodal Synergistic Framework for Prognostication of Diabetes Mellitus

Diabetes is a chronic medical condition that affects millions of people worldwide. It can lead to many complications such as heart disease, nerve problems, kidney damage and blindness. So that Early detection and effective management will reduce the complications. By analyzing the clinical data and lifestyle data such as Glucose levels, Body Mass Index (BMI), Blood pressure, Insulin, Diabetes pedigree, Pregnancies, Age, Skin thickness etc. By using this data we are implementing several Machine Learning models like Logistic regression,K-Nearest Neighbors, Support vector machine, Decision tree, Random forest, Boosting techniques like AdaBoost,GradientBoosting,StochasticGradientboosting,CatB oostclassifier,XGBoost,Light GBM. Now we get the accuracy score for above models and we applying a Stacking technique to increase the accuracy score. The primary goal of this project is to build an effective Machine Learning model that classifies individuals as either person is diabetic or non-diabetic. By using a combination of tools such as Python, Pandas, NumPy and Scikit-Learn etc. These tools will be employed to preprocess the data, relevant features, train the classification models and evaluate their performance to ensure the better prediction. It provides an efficient and scalable solution for diabetes prediction contributing to the early detection and improving the patient outcomes.