Diabetes Prediction: Machine Learning Models Enhanced by Harmony Search Optimization

The Harmony Search (HS) algorithm has been used in many applications, including engineering, excavation, epidemiology, object recognition, stock price prediction, genetic analysis, and structural design optimization, all derived from the improvisation process in musicians. The study covers the application of the HS algorithm for a predictive model of diabetes with its parameter tuning and optimization process of the model. In performing the research, a dataset will be used that contains data of information regarding demographic data, clinical metrics, and lifestyle attributes of people with and without diabetes. The most important features such as age, BMI, blood pressure, and glucose level will be treated as input variables. The HS algorithm will optimize the model parameters to develop the best model for the process of prediction of diabetes. The research has four parts: preparing, choosing features, training models, HS-driven upgrade, and checking results. The best model with K-NN beat all basic models in accuracy, sensitivity, and specificity. This shows good risk evaluation and early diabetes detection. This article contributes new contributions to emerging areas of diabetes prediction through novel avenues in model optimization. Specifically, this shows the efficacy of the HS algorithm, which improves the performance of predictive models. In that sense, this improves the ability to do an accurate and reliable risk evaluation of diabetes in clinical applications.