Diastolic Dysfunction Prediction with Symptoms Using Machine Learning Approach
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Published:2022-06-30
Issue:3
Volume:4
Page:714-726
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ISSN:2618-1630
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Container-title:Vol 4 Issue 3
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language:en
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Short-container-title:IJIST
Author:
Anjum Muhammad Shoaib1, Riaz Omer2, Latif Muhammad Salman3
Affiliation:
1. Department Of Computer Science, The Islamia University Of Bahawalpur 2. Department of Information Technology, The Islamia University of Bahawalpur, Bahawalpur, Pakistan 3. Department of Computer Science, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
Abstract
Cardiac disease is the major cause of deaths all over the world, with 17.9 million deaths annually, as per World Health Organization reports. The purpose of this study is to enable a cardiologist to early predict the patient’s condition before performing the echocardiography test. This study aims to find out whether diastolic function or diastolic dysfunction using symptoms through machine learning. We used the unexplored dataset of diastolic dysfunction disease in this study and checked the symptoms with cardiologist to be enough to predict the disease. For this study, the records of 1285 patients were used, out of which 524 patients had diastolic function and the other 761 patients had diastolic dysfunction. The input parameters considered in this detection include patient age, gender, BP systolic, BP diastolic, BSA, BMI, hypertension, obesity, and Shortness of Breath (SOB). Various machine learning algorithms were used for this detection including Random Forest, J.48, Logistic Regression, and Support Vector Machine algorithms. As a result, with an accuracy of 85.45%, Logistic Regression provided promising results and proved efficient for early prediction of cardiac disease. Other algorithms had an accuracy as follow, J.48 (85.21%), Random Forest (84.94%), and SVM (84.94%). Using a machine learning tool and a patient’s dataset of diastolic dysfunction, we can declare either a patient has cardiac disease or not.
Subject
Computer Networks and Communications,Hardware and Architecture,Software
Reference48 articles.
1. Bharti, R., Khamparia, A., Shabaz, M., Dhiman, G., Pande, S., and Singh, P.: "Prediction of heart disease using a combination of machine learning and deep learning", "Computational intelligence and neuroscience", 2021, 2021 2. Akinosun, A.S., Polson, R., Diaz-Skeete, Y., De Kock, J.H., Carragher, L., Leslie, S., Grindle, M., and Gorely, T.: "Digital technology interventions for risk factor modification in patients with cardiovascular disease: systematic review and meta-analysis", "JMIR mHealth and uHealth", 2021, 9, (3), pp. e21061 3. Dandel, M., and Hetzer, R.: "Ventricular systolic dysfunction with and without altered myocardial contractility: clinical value of echocardiography for diagnosis and therapeutic decision-making", "International Journal of Cardiology", 2021, 327, pp. 236-250 4. Biondi, B., Fazio, S., Palmieri, E.A., Carella, C., Panza, N., Cittadini, A., Bonè, F., Lombardi, G., and Saccà, L.: "Left ventricular diastolic dysfunction in patients with subclinical hypothyroidism", "The Journal of Clinical Endocrinology & Metabolism", 1999, 84, (6), pp. 2064-2067 5. https://www.upmc.com/services/pulmonology/conditions/diastolic-dysfunction#:~:text=When%20the%20muscles%20of%20the,back%20up%20in%20the%20organs, accessed 06-Mar-2022
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