Diabetes Prediction using Hybrid Model

Diabetes prediction plays a crucial role in early intervention and effective management of the disease. This research paper presents a novel approach to diabetic prediction by proposing a hybrid machine learning model that combines the strengths of different algorithms. The hybrid model integrates ensemble techniques with deep learning methods to enhance prediction accuracy and interpretability. The study involves data collection, preprocessing, feature selection, model design, and extensive experimentation. Results demonstrate the superiority of the hybrid model over individual algorithms and showcase its potential for accurate diabetic prediction. Furthermore, the interpretability of the hybrid model is explored, providing insights into the key risk factors contributing to the predictions. The research contributes to the advancement of diabetic prediction and highlights the efficacy of hybrid models in healthcare applications. Also, the accuracy of the hybrid model used is 80 percent.