HEART DISEASE PREDICTION USING AN ENHANCED SELF-ADAPTIVE BAYESIAN ALGORITHM
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Heart disease is currently the leading cause of death on a global scale. Predicting cardiovascular disease is a challenging task since one need significant experience in addition to current knowledge of the most recent developments in the field. Cardiovascular disease can be hard to diagnose since it has numerous warning signs, including high blood pressure, high cholesterol levels, diabetes, ventricular fibrillation, and additional reasons. The seriousness of individual cardiac diseases is being evaluated using several kinds of data analysis and neural network methods. Numerous techniques, including the k-nearest-neighbor algorithm (KNN), genetic algorithms (GA), and Naive Bayes (NB), are used to categorize the extent of the illness. Heart disease requires meticulous management due to its intricate nature. Data mining and medical research perspectives are utilized to detect many types of metabolic diseases. Data mining with classification has considerable benefits for both data analysis and the prediction of heart disease. In this study, an enhanced self-adaptive Bayesian method (ESABA) for predicting heart disease is devised. Pre-processing is the first stage of the diagnostic procedure. There are three processes involved: replacement of absent qualities, elimination of duplication, and division. The parameter's vacant value is modified once the patient as a whole has been established, together with the individual's age group, fat stage, and heart rate. Once the patient as a whole, as well as the individual's age group, fat stage, and heart rate, have been established, the parameter's vacant value is modified. If the majority of a patient's attribute values match, their attribute value is substituted in the same spot. To decrease the amount of data, irrelevant or redundant attributes are eliminated as part of the redundancy elimination method. The whole experiment was carried out in MATLAB. Environment. Precision, recall, F1 score, and accuracy were used to evaluate the efficacy of the claimed mechanism in determining whether the heart data acquired is abnormal or normal, utilizing an improved implementation of a dynamically enhanced self-adaptive Bayesian algorithm (ESABA)method.
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