Machine Learning Models Predicting the Survival of Abrupt Heart Attack in Cardiac Sarcoidosis Disease using the Wearable Device
Jana Shafi1, P. Venkata Krishna2

1Jana Shafi*, Research Scholar, Department of Computer and Engineering Science, SPMVV University Tirupati, A.P., India.
2P.Venkata Krishna, Department of Computer Science and Engineering, SPMVV University, Tirupati, A.P, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 20, 2020. | Manuscript published on March 10, 2020. | PP: 9-13 | Volume-9 Issue-5, March 2020. | Retrieval Number: D2076029420/2020©BEIESP | DOI: 10.35940/ijitee.D2076.039520
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Abstract: Wearable technology has countless prospects of remodelling healthcare establishment and also medical education. Cardiac sarcoidosis disease (CS) is a sporadic illness in which white blood cells (WBC) clusters known as granulomas, form as heart tissue. Cardiac sarcoidosis disease (CS) Patients are at high threat of ventricular tachycardia or ventricular fibrillation (VT/VF). Wearable cardioverter defibrillator device is introduced which helps to alleviate the abrupt heart attack risk amid patients of cardiac sarcoidosis. A reflective evaluation of the commercial record acknowledged patients of cardiac sarcoidosis disease who sported the wearable cardioverter defibrillator (WCD). ML models are applied to get accurate predictions to motivate WCD wear ability. The wearable device cardioverter defibrillator (WCD) was worn by forty six patients of cardiac sarcoidosis disease in which 22(48%) female, male 24 (52%). The wearable cardioverter defibrillator (WCD) was sported hours about 23.6 median daily. Nearby eleven ventricular tachycardia or ventricular fibrillation (VT/VF) incidents occur in ten patients (22%). Ventricular tachycardia or ventricular fibrillation (VT/VF) happened over a series of (1-79) days, median of twenty-four days. 1st- heart attack success for ventricular tachycardia or ventricular fibrillation (VT/VF) conversion was hundred percent. Survival of Patient in twenty four hours after treatment of attack was hundred percent. To regulate the discontinuing cause for wearable device cardioverter defibrillator (WCD) use specified that among seven attacked patients received ICD, one patient was died two weeks later discontinuing the use of wearable cardioverter defibrillator device (WCD), and two patients were absent to track. Sixteen were not attacked patients, who obtained an implantable cardioverter defibrillator (ICD) while seven of them attained and improved left ventricular ejection fraction (LVEF). Abrupt heart attack (HA) management amongst patients of cardiac sarcoidosis disease (CS) was assisted by wearable device cardioverter defibrillator (WCD) ensuing in positive ventricular tachycardia or ventricular fibrillation (VT/VF) termination upon attack delivery. In this paper, the dataset is retrieved from google dataset search and evaluated on various ML models to predict the survival of the patients Receiving ICD while wearing WCD as well as evaluating the developed model performance and to identify the best applicable model. Dataset is primarily processed and nursed to many machine learning classifiers like KNN, SVM, Perceptron, Random Forest, Decision Trees (DT), Logistic Expression, SGD, and Naïve Basis. Cross-validation is smeared, training is performed so that new machine learning models are established and verified. The outcomes found are assessed on many factors such as Accuracy, Misclassification Rate, True Positive Rate, True Negative Rate, Precision, Prevalence, False Positive rate taken to build the model. Result analysis reveals that among all the classifiers SVM and KNN best model acquiescent high and precise outcomes. 
Keywords: WCD, Wearable, WIOMT, IoT, SCA,SCD, CS,HA
Scope of the Article: Machine Learning