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دسته بندی:
یادگیری ماشین - machine learning
سال انتشار:
2019
عنوان انگلیسی مقاله:
Predictive model of cardiac arrest in smokers using machine learning technique based on Heart Rate Variability parameter
ترجمه فارسی عنوان مقاله:
مدل پیش بینی ایست قلبی در افراد سیگاری با استفاده از روش یادگیری ماشین بر اساس پارامتر تنوع ضربان قلب
منبع:
Sciencedirect - Elsevier - Applied Computing and Informatics, Corrected proof: doi:10:1016/j:aci:2019:06:002
نویسنده:
Shashikant R. ⇑, Chetankumar P.
چکیده انگلیسی:
Cardiac arrest is a severe heart anomaly that results in billions of annual casualties. Smoking is a specific
hazard factor for cardiovascular pathology, including coronary heart disease, but data on smoking and
heart death not earlier reviewed. The Heart Rate Variability (HRV) parameters used to predict cardiac
arrest in smokers using machine learning technique in this paper. Machine learning is a method of computing
experience based on automatic learning and enhances performances to increase prognosis. This
study intends to compare the performance of logistical regression, decision tree, and random forest
model to predict cardiac arrest in smokers. In this paper, a machine learning technique implemented
on the dataset received from the data science research group MITU Skillogies Pune, India. To know the
patient has a chance of cardiac arrest or not, developed three predictive models as 19 input feature of
HRV indices and two output classes. These model evaluated based on their accuracy, precision, sensitivity,
specificity, F1 score, and Area under the curve (AUC). The model of logistic regression has achieved an
accuracy of 88.50%, precision of 83.11%, the sensitivity of 91.79%, the specificity of 86.03%, F1 score of
0.87, and AUC of 0.88. The decision tree model has arrived with an accuracy of 92.59%, precision of
97.29%, the sensitivity of 90.11%, the specificity of 97.38%, F1 score of 0.93, and AUC of 0.94. The model
of the random forest has achieved an accuracy of 93.61%, precision of 94.59%, the sensitivity of 92.11%,
the specificity of 95.03%, F1 score of 0.93 and AUC of 0.95. The random forest model achieved the best
accuracy classification, followed by the decision tree, and logistic regression shows the lowest classification
accuracy.
Keywords: Cardiac arrest | Heart Rate Variability | Machine learning | Accuracy | Precision | Area under the curve
قیمت: رایگان
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