Prasanna Kottapalle1,*, Tan Kuan Tak2, Pravin Ramdas Kshirsagar3, Gopichand Ginnela4, Vijaya Krishna Akula5
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3857-3892, 2025, DOI:10.32604/cmc.2025.065287
- 03 July 2025
Abstract Heart failure prediction is crucial as cardiovascular diseases become the leading cause of death worldwide, exacerbated by the COVID-19 pandemic. Age, cholesterol, and blood pressure datasets are becoming inadequate because they cannot capture the complexity of emerging health indicators. These high-dimensional and heterogeneous datasets make traditional machine learning methods difficult, and Skewness and other new biomarkers and psychosocial factors bias the model’s heart health prediction across diverse patient profiles. Modern medical datasets’ complexity and high dimensionality challenge traditional prediction models like Support Vector Machines and Decision Trees. Quantum approaches include QSVM, QkNN, QDT, and others.… More >
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