The BagMOOV ensemble technique gives accurate and efficient results in all the datasets compared to the other state of art techniques. The task is to predict whether a patient has a heart disease based on their profile and a series of medical tests. Google Scholar. Topological Data Analysis for Classification of Heart Disease Data ... Table 9 shows the comparison between Statlog and Cleveland datasets. Bayes and Decision tree. In this research, to emphasize the importance of TDA in medical field, the mapper algorithm was used for predicting heart disease by using two UCI heart disease datasets (Cleveland and Statlog). First, we'll load the data into a data frame and rename the columns according to … Related Papers . Heart Disease Prediction Using Deep Neural Networks: A Novel … 机器学习_Statlog (Heart) Data Set (Statlog (心脏) 数据集) This dataset classifies people described by a set of attributes as good or bad credit risks. The goal is to be able to accurately classify as having or not having heart disease based on … Dataset 43 classifiers for the prediction and diagnosis of heart disease, obtaining a classification percentage of 44 99.2% using the Decision Tree technique, out of 96.5% classification using the Naive Bayes technique. The dataset utilized are the Cleveland Heart Database and Statlog Database taken from UCI Machine learning dataset vault. knn的matlab实验【使用UCI数据集】3.0. The result was a four-HL DNN that detected coronary heart diseases with promising results . 中文关键词：. UCI 心脏病数据集Heart Disease Data Set. Heart

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