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Smartphone Based Ischemic Heart Disease (Heart Attack) Risk Prediction using Clinical Data and Data Mining Approaches, a Prototype Design

机译:基于智能手机的缺血性心脏病(心脏病发作)风险预测使用临床数据和数据挖掘方法,一种原型设计

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We developed a simple approach to predict risk of developing Ischemic Heart Disease (IHD) (Heart Attack) using smartphone. An Android based prototype software has been developed by integrating clinical data obtained from patients admitted with IHD. The clinical data from 787 patients has been analyzed and correlated with the risk factors like Hypertension, Diabetes, Dyslipidemia (Abnormal cholesterol), Smoking, Family History, Obesity, Stress and existing clinical symptom which may suggest underlying non detected IHD. The data was mined with data mining technology and a score is generated. Risks are classified into low, medium and high for IHD. On comparing and categorizing the patients whose data is obtained for generating the score; we found there is a significant correlation of having a cardiac event when low & high and medium & high category are compared ; p=0.0001 and 0.0001 respectively. Our research is to make simple approach to detect the IHD risk and aware the population to get themselves evaluated by a cardiologist to avoid sudden deaths. Currently available tools has some limitations which makes them underutilized by population. Our research product may reduce this limitation and promote risk evaluation on time.
机译:我们开发了一种简单的方法来预测使用智能手机发展缺血性心脏病(IHD)(心脏病发作)的风险。通过整合从IHD录取的患者获得的临床数据开发了基于Android的原型软件。来自787名患者的临床资料已经分析和相关,与高血压,糖尿病,血脂血症(异常胆固醇),吸烟,家族史,肥胖,压力和现有临床症状等危险因素相关,这些临床症状可能表明未检测到的IHD。数据采用数据挖掘技术开采,生成分数。风险被分为低,中和高度。比较和分类数据获得得分的数据;我们发现在比较低和高中和高类别时具有心脏事件的显着相关性; P = 0.0001和0.0001分别。我们的研究是使探测IHD风险的简单方法,并意识到人口通过心脏病学家评估,以避免突然死亡。目前可用的工具具有一些限制,使其能够通过人口未充分利用。我们的研究产品可能会降低此限制并促进风险评估。

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