确定心脏病分类最佳机器学习算法的分析

Y. Kale, S. Rathkanthiwar, Sarvadnya Rajurkar, Himanshu Parate, Anshul Ninawe, Aditya Bharti
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引用次数: 0

摘要

医疗保健部门生成大量数据点,并使用某些程序进行处理。处理数据的方法有很多,其中数据挖掘是常用的方法之一。心脏病是全球死亡的主要原因。这个项目确定了预测心脏病可能性的系统的最佳算法。该系统的结果提供了患心脏病的可能性百分比。使用医学参数对数据集进行分类。为了分析这些因素,我们的系统采用了数据挖掘分类方法。使用Naïve贝叶斯,逻辑回归,随机森林,k近邻,XGboost,决策树和支持向量机,混合分类器和神经网络的机器学习算法对数据集进行分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis for Determining Best Machine learning Algorithm for Classification of Heart Diseases
Numerous data points are generated by the healthcare sector and processed using certain procedures. There are many methods for processing a data among which data mining is one of the methods frequently employed. Heart condition is the main cause of death in the globe. This project determines the best algorithm for the system that anticipates the possibility of cardiac disease. The outcomes of this system provide the likelihood in percentage of acquiring heart disease. The datasets are categorised using medical parameters. To analyse such factors, our system employs a data mining classification method. The datasets are analysed using Naïve Bayes, Logistic Regression, Random Forest, K-Nearest Neighbour, XGboost, Decision Tree and Support Vector Machine, Machine learning algorithms with hybrid Classifiers and Neural Network.
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