A Survey on Heart Disease Prediction Techniques Using Machine Learning and the Scope of Hybrid Quantum Machine Learning

This survey has reviewed the landscape of cardiac risk prediction methods spanning probabilistic classifiers, tree-based partitioners, proximity learners, margin-based discrim-inators, ensemble aggregators, connectionist networks, deep representation learners, and optimization-driven feature selec-tors. A consistent picture emerges: accuracy has improved incrementally across a succession of methodological advances, yet overfitting, dependence on careful feature engineering, limited external validity, and computational overhead persist as unresolved barriers. The review additionally identifies a conspicuous gap in comparative studies that directly evaluate classical supervised learning against hybrid quantum-classical pipelines. Filling this gap could establish whether quantum-augmented architectures offer practical improvements in ac-curacy, calibration, and cross-cohort generalization for cardiac prediction tasks.

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