A Hybrid Machine Learning Optimized Algorithm for Type 2 Diabetes Mellitus Prediction - Raja S (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Investigating a healthcare system employing contemporary computing technology is a prominent field of inquiry within healthcare research. Researchers in the technology and healthcare domain are continuously collaborating to enhance the technological preparedness of these systems. Diabetes is widely recognized as a highly consequential and enduring ailment, giving rise to various complications, including but not limited to visual impairment, limb loss, and cardiovascular disorders. Numerous countries are actively working to mitigate the impact of this disease by implementing early-stage preventive measures, which involve identifying and prognosticating diabetes symptoms through diverse diagnostic approaches. This research activity aims to construct a very accurate model for the early prognosis of Type 2 Diabetes Mellitus (T2-DM). This paper presents a new hybrid clustering model that seeks to improve the integration between the Particle Swarm Optimization (PSO) technique for feature optimization and the Fuzzy Clustering Means (FCM) algorithm for effective clustering. This approach aims to improve the precision, responsiveness, and selectivity of the clustering procedure. The study conducted a comparative analysis of the proposed model's accuracy, sensitivity, and specificity metrics about existing hybrid approaches, such as K means-C4.5 and ANN+FNN, which are considered state-of-the-art in the field.