2019 · 127 citations · 8 references
ComputingCardiologyComputer ScienceClinical Decision SupportData MiningMachine LearningClassificationComputer-aided DiagnosisDecision Support In HealthcareDecision TreesPredictive Health ModelingRisk PredictionAccuracyClassification AlgorithmsData ClassificationDecision Tree ClassificationMachine Learning ClassificationMachine Learning For DiagnosisNaive BayesNaive Bayes Classification
Health care generates vast data that are processed using techniques such as data mining, and heart disease remains the leading cause of death worldwide. The study aims to predict the likelihood of heart disease. The system uses Python to apply Decision Tree and Naive Bayes classifiers on medical‑parameter datasets, selecting the algorithm with the highest accuracy. The system outputs percentage probabilities of heart disease occurrence.
Health care field has a vast amount of data, for processing those data certain techniques are used. Data mining is one of the techniques often used. Heart disease is the Leading cause of death worldwide. This System predicts the arising possibilities of Heart Disease. The outcomes of this system provide the chances of occurring heart disease in terms of percentage. The datasets used are classified in terms of medical parameters. This system evaluates those parameters using data mining classification technique. The datasets are processed in python programming using two main Machine Learning Algorithm namely Decision Tree Algorithm and Naive Bayes Algorithm which shows the best algorithm among these two in terms of accuracy level of heart disease.
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