Heart disease prediction using naive bayes algorithm. This revised dataset is used to tr...
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Heart disease prediction using naive bayes algorithm. This revised dataset is used to train the models, and standard metrics The main goal of this system is to predict heart disease using data mining technique such as Naive Bayesian Algorithm. The system uses 14 medical parameters such as age, sex, blood pressure, cholesterol, and obesity for prediction. Because of its simplicity and efficiency, Naive Bayes is widely used in tasks such as text classification, spam detection, sentiment analysis, and medical data classification. Raw hospital data set is used and then preprocessed and transformed the data set. GWO is a metaheuristic algorithm that can be This can be done by using various inference algorithms, such as variable elimination or belief propagation. In this study, a Heart Disease Prediction System (HDPS) is developed using Naive Bayes algorithm for predicting the risk level of heart disease. Naive bayes classifier implemented from scratch without the use of any standard library and evaluation on the dataset available from UCI. The HDPS predicts the likelihood of patients getting heart disease. Comparison of this model is made with An Intelligent Heart Disease Prediction System (IHDPS) [4] is developed by using data mining techniques Naive Bayes, Neural Network, and Decision Trees was proposed by Sellappan Palaniappan. Sadly, these knowledges are rarely accustomed support clinical decision-making.
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