Enhanced Heart Disease Classification Using Parallelization and Integrated Machine-Learning Techniques

The progressive application of machine learning in disease prediction within the realm of medical diagnosis is witnessing notable advancements. This remarkable evolution can be predominantly attributed to the substantial enhancements in disease identification and recognition systems, which furnish invaluable data facilitating the early detection of perilous ailments. Consequently, this pivotal development has yielded a momentous upsurge in the survival rates of patients. To augment disease prognosis, our study employs a diverse array of algorithms, each harnessing unique advantages, across three distinct disease databases sourced from the esteemed UCI repository. Complementing this methodology, we employ a meticulous feature selection process, leveraging backward modeling and rigorous statistical tests for each dataset. The empirical results derived from this study unequivocally reinforce the efficacy of machine learning in early disease detection. Notably, our system manifests the convergence of a support vector machine, KNN and an artificial neural network, both adeptly trained on comprehensive datasets replete with spectral information and meticulously engineered algorithms with parallel processing techniques to reduce training time for quick results Within the realm of data processing, the prediction of heart disease emerges as an intricate and riveting pursuit. The inherent scarcity of specialized medical professionals compounded by a disconcerting prevalence of erroneous diagnoses necessitates the development of an expeditious and efficient detection system. Intriguingly, prior systems have demonstrated the immense potential of amalgamating clinical decision support with computer-based patient records, thus engendering a tangible reduction in medical errors and concomitantly refining patient safety.