2016 · 244 citations · 7 references
ComputingCardiologyHealth InformaticsClinical Decision SupportData MiningHealth Data ScienceCardiovascular DiseaseData PreprocessingDecision Support AnalyticsDecision Support In HealthcareDecision TreesDiagnostic Decision SupportDecision Support System ModelsDecision Tree ClassificationKnowledge Discovery ProcessesMachine Learning Decision Support
Heart disease prevalence is rising worldwide, driven by lifestyle and genetic factors, with key risk indicators including blood pressure, cholesterol, and pulse rate. This study surveys classification methods for predicting individual heart disease risk from age, gender, blood pressure, cholesterol, and pulse rate. Risk levels are classified using data‑mining algorithms such as Naïve Bayes, KNN, decision trees, and neural networks. The models achieve high accuracy, especially when multiple attributes are included.
Nowadays, health disease are increasing day by day due to life style, hereditary. Especially, heart disease has become more common these days, i.e. life of people is at risk. Each individual has different values for Blood pressure, cholesterol and pulse rate. But according to medically proven results the normal values of Blood pressure is 120/90, cholesterol is and pulse rate is 72. This paper gives the survey about different classification techniques used for predicting the risk level of each person based on age, gender, Blood pressure, cholesterol, pulse rate. The patient risk level is classified using datamining classification techniques such as Naïve Bayes, KNN, Decision Tree Algorithm, Neural Network. etc., Accuracy of the risk level is high when using more number of attributes.
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