Predictive Model for Diabetic Re-admission Using Neural Network Techniques

From millions of people suffering from diabetes worldwide, proper management would translate to reducing complications of readmission to hospitals, thereby further reducing medical and insurance costs. The hospital readmissions also determine the reputation of the hospitals and their cost implications, and they are also a very good barometer of problems in the health sector. It therefore sought to apply neural network models for an analysis and forecasting of the readmission rates of patients with diabetes in the hospital. The dataset provided contains many attributes regarding patient demographics, medical conditions, and the drugs that are filtered and processed to deal with missing values, reduce complexity, and also to change categorical variables into numerical form. Techniques like Random Forest, Neural Networks, and Support Vector Machines are used after feature engineering to predict whether a patient is readmitted or not. Accuracy, classification reports, and confusion matrices are used to evaluate the models. More complex neural networks, such as ANN and LSTM models, work for the sake of even more accurate prediction-making. The overall findings of the final models regarding health care providers range from identifying high-risk patients to improving patient care and reducing readmission-related healthcare expenses.