FUZZY SEA HORSE OPTIMIZATION ALGORITHM (FSHOA) AND QUINE MCCLUSKEY ENSEMBLE CLASSIFIER (QMEC) FOR HEART DISEASE PREDICTION
DOI:
https://doi.org/10.48047/2q1m6t27Keywords:
Fuzzy, Sea Horse, Optimization, Quine, Mccluskey, Ensemble, Classifier, Heart Disease, Prediction, Support Vector MachineAbstract
Cardiovascular disease is the primary reason for mortality worldwide, responsible for around a third of all deaths. To assist medical professionals in quickly identifying and diagnosing patients, numerous machine learning and data mining techniques are utilized to predict the disease. Many researchers have developed various models to boost the efficiency of these predictions. Feature selection and extraction techniques are utilized to remove unnecessary features from the dataset, thereby reducing computation time and increasing the efficiency of the models.
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References
Korial, A. E., Gorial, I. I., & Humaidi, A. J. (2024). An Improved Ensemble-Based Cardiovascular Disease Detection System with Chi-Square Feature Selection. Computers, 13(6), 126.
Gupta, I., Bajaj, A., & Sharma, V. (2024). Comparative analysis of machine learning algorithms for heart disease prediction. International Journal of Hybrid Intelligent Systems, (Preprint), 1-15.
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