Muhammad Shoaib Anjum, Dr. Shahzad Mumtaz, Dr. Omer Riaz, Waqas Sharif
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引用次数: 0
摘要
医疗保健行业拥有大量的患者健康记录,但缺乏使用数据挖掘技术发现隐藏信息的方法。数据挖掘及其算法可以在这种情况下提供帮助。本研究旨在发现症状的隐藏模式,以便在使用运动耐量试验(ETT)前发现早期应激超声心动图。在本研究中,776名患者的原始ETT数据来自巴基斯坦南旁遮普省巴哈瓦尔布尔的私人心脏诊所“The heart Center Bahawalpur”。杜克跑步机评分(Duke treadmill score, DTS)是ETT的一种输出,用于对患者心脏工作正常或异常进行分类。在这项工作中,使用支持向量机(SVM)、Logistic回归(LR)、J.48和随机森林(RF)等多种机器学习算法,利用患者的一般信息,如性别、年龄、体表面积(BSA)、体重指数(BMI)、血压(BP)收缩压、舒张压等,以及糖尿病、家族史、高血压、肥胖、老年、绝经后、吸烟者、糖尿病、糖尿病、糖尿病、糖尿病、糖尿病等危险因素信息,对患者的心脏工作正常与否进行分类。胸痛和呼吸短促(SOB)。在本研究中,使用分割百分比为60-40的Logistic回归算法可以达到85.16%的最佳准确率。
Heart Attack Risk Prediction with Duke Treadmill Score with Symptoms using Data Mining
The healthcare industry has a huge volume of patients’ health records but the discovery of hidden information using data mining techniques is missing. Data mining and its algorithm can help in this situation. This study aims to discover the hidden pattern from symptoms to detect early Stress Echocardiography before using Exercise Tolerance Test (ETT). During this study, raw ETT data of 776 patients are obtained from private heart clinic “The Heart Center Bahawalpur”, Bahawalpur, South Punjab, Pakistan. Duke treadmill score (DTS) is an output of ETT which classifies a patient’s heart is working normally or abnormally. In this work multiple machine learning algorithms like Support Vector Machine (SVM), Logistic Regression (LR), J.48, and Random Forest (RF) are used to classify patients’ hearts working normally or not using general information about a patient like a gender, age, body surface area (BSA), body mass index (BMI), blood pressure (BP) Systolic, BP Diastolic, etc. along with risk factors information like Diabetes Mellitus, Family History, Hypertension, Obesity, Old Age, Post-Menopausal, Smoker, Chest Pain and Shortness Of Breath (SOB). During this study, it is observed that the best accuracy of 85.16% is achieved using the Logistic Regression algorithm using the split percentage of 60-40.