Chronic diseases are among the most frequent major health concerns. Early detection of chronic illnesses can help to avoid or lessen their repercussions, potentially lowering death rates. It's an innovative technique to use machine learning algorithms to identify dangerous variables. The problem with existing feature selection procedures is that each method gives a unique collection of features that influence model validity, and current methods are incapable of performing effectively on large multidimensional datasets. We would want to present a novel model that uses a feature selection strategy to choose ideal features from large multidimensional data sets to deliver credible forecasts of chronic diseases while preserving the uniqueness of the data. To assure the success of our proposed model, we used balanced classes by applying hybrid balanced class sampling methods to the original dataset, as well as methods to provide valid data for the training model, characterization and data conversion are required. Our model was run and assessed on datasets with binary and multi-valued classifications. We utilized a variety of datasets (Parkinson's disease, arrhythmia, breast cancer, kidney disease, and diabetes). To select suitable features, the hybrid feature model is used, which includes six ensemble models and involves voting on attributes. The accuracy of the original dataset before applying the framework is recorded and compared to the accuracy of the reduced set of characteristics. The findings are given individually to allow for comparisons. We can conclude from the results that our proposed model performed best on multi-valued class datasets rather than binary class characteristics.