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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:使用临床数据和数据挖掘方法的基于智能手机的缺血性心脏病(心脏病发作)风险预测,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生缺血性心脏病(IHD)(心脏病发作)的风险。通过整合从IHD住院患者获得的临床数据,开发了基于Android的原型软件。已对787例患者的临床数据进行了分析,并将其与诸如高血压,糖尿病,血脂异常(胆固醇异常),吸烟,家族史,肥胖,压力和现有临床症状等危险因素相关联,这些潜在症状可能提示潜在的未检测出IHD。使用数据挖掘技术对数据进行挖掘,并生成一个分数。 IHD的风险分为低,中和高。在对获得数据以生成分数的患者进行比较和分类时;当比较低,高和中,高类别时,我们发现发生心脏事件有显着相关性; p分别为0.0001和0.0001。我们的研究将提供一种简单的方法来检测IHD风险并了解人群,以使心脏病专家对自己进行评估,从而避免猝死。当前可用的工具有一些局限性,这使得它们未被人群充分利用。我们的研究产品可能会减少此限制并促进按时进行风险评估。

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