Prediction of Diabetes Healthcare Disease using Machine Learning Techniques

Diabetes is a disease that can develop in a person if their blood glucose levels are consistently high. Diabetes is a severe condition that, if left untreated, can lead to several serious complications in a person, including issues relating to the heart, problems with the kidneys, high blood pressure, and damage to the eyes. Diabetes can also have an effect on other organs in the human body. If diabetes is diagnosed at an earlier stage, it may be more easily managed. To accomplish this mission, the work that we are doing for this project entails making an early prediction of diabetes in a human body or a patient for a higher accuracy level using various machine learning techniques. By constructing models using the datasets collected from patients, machine learning algorithms produce more accurate results when used for prediction. Predicting diabetes using a PIMA dataset with the help of machine learning-based boosting classifiers, such as CatBoost and Adaboost, will be the focus of this work. Compared with other methods, each model's accuracy varies in its unique way. The work done on the project provides an accurate or higher-accuracy model, demonstrating that the model can accurately predict diabetes. According to the results of our analysis, AdaBoost obtained a higher level of accuracy when compared to other methods of ML.