Heart Disease Prediction using Chi-Square Test and Linear Regression

IF 1.7 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Dinesh Kalla, Arvind Chandrasekaran
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Abstract

Heart disease is most common disease reported currently in the United States among both the genders and according to official statistics about fifty percent of the American population is suffering from some form of cardiovascular disease. This paper performs chi square tests and linear regression analysis to predict heart disease based on the symptoms like chest pain and dizziness. This paper will help healthcare sectors to provide better assistance for patients suffering from heart disease by predicting it in beginning stage of disease. Chi square test is conducted to identify whether there is a relation between chest pain and heart disease cases in the United States by analyzing heart disease dataset from IEEE Data Port. The test results and analysis show that males in the United States are most likely to develop heart disease with the symptoms like chest pain, dizziness, shortness of breath, fatigue, and nausea. This test also shows that there is a week corelation of 0.5 is identified which shows people with all ages including teens can face heart diseases and its prevalence increase with age. Also, the tests indicate that 90 percent of the participant who are facing severe chest pain is suffering from heart disease where majority of the successful heart disease identified is in males and only 10 percent participants are identified as healthy. The evaluated p-values are much greater than the statistical threshold of 0.05 which concludes factors like sex, Exercise angina, Cholesterol, old peak, ST_Slope, obesity, and blood sugar play significant role in onset of cardiovascular disease. We have tested the dataset with prediction model built on logistic regression and observed an accuracy of 85.12 percent.
用卡方检验和线性回归预测心脏病
据报道,心脏病是目前在美国男女中最常见的疾病,根据官方统计,大约50%的美国人口患有某种形式的心血管疾病。本文通过卡方检验和线性回归分析,根据胸痛、头晕等症状预测心脏病。本文将帮助医疗保健部门提供更好的援助,患者患心脏病的预测,在疾病的开始阶段。通过分析IEEE数据端口的心脏病数据集,对美国胸痛与心脏病病例之间是否存在关联进行卡方检验。测试结果和分析表明,美国男性最有可能患上心脏病,并伴有胸痛、头晕、呼吸急促、疲劳和恶心等症状。该测试还表明,每周相关性为0.5,这表明包括青少年在内的所有年龄段的人都可能面临心脏病,其患病率随着年龄的增长而增加。此外,测试表明,90%面临严重胸痛的参与者患有心脏病,而大多数成功确定的心脏病患者是男性,只有10%的参与者被确定为健康的。评价的p值均大于统计学阈值0.05,表明性别、运动性心绞痛、胆固醇、old peak、ST_Slope、肥胖、血糖等因素对心血管疾病的发生有显著影响。我们使用基于逻辑回归的预测模型对数据集进行了测试,准确率为85.12%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
1.40
自引率
0.00%
发文量
22
审稿时长
4 weeks
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