Predicting stroke and mortality in mitral stenosis with atrial flutter: A machine learning approach,Annals of Noninvasive Electrocardiology

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Predicting stroke and mortality in mitral stenosis with atrial flutter: A machine learning approach
Annals of Noninvasive Electrocardiology ( IF 1.1 ) Pub Date : 2023-08-06 , DOI: 10.1111/anec.13078
Amer Rauf ₁ , Asif Ullah ₂ , Usha Rathi ₃ , Zainab Ashfaq ₄ , Hidayat Ullah ₅ , Amna Ashraf ₆ , Jateesh Kumar ₇ , Maria Faraz ₈ , Waheed Akhtar ₉ , Amin Mehmoodi ₁₀ , Jahanzeb Malik _{1,

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Affiliation

Department of Electrophysiology, Armed Forces Institute of Cardiology, Rawalpindi, Pakistan.
Department of Cardiology, KMU Institute of Medical Sciences, Kohat, Pakistan.
Abbasi Shaheed Hospital, Karachi, Pakistan.
Department of Medicine, CMH Lahore Medical College, Lahore, Pakistan.
Department of Cardiology, Pakistan Atomic Energy Commission Hospital, Islamabad, Pakistan.
Department of Medicine, Military Hospital Rawalpindi, Rawalpindi, Pakistan.
Department of Medicine, Jinnah Sindh Medical University, Karachi, Pakistan.
Department of Business Development, Bahria University, Islamabad, Pakistan.
Department of Cardiology, Abbas Institute of Medical Sciences, Muzaffarabad, Pakistan.
Department of Medicine, Ibn e Seena Hospital, Kabul, Afghanistan.
Department of Cardiovascular Research, Cardiovascular Analytics Group, Rawalpindi, Pakistan.

Our study hypothesized that an intelligent gradient boosting machine (GBM) model can predict cerebrovascular events and all-cause mortality in mitral stenosis (MS) with atrial flutter (AFL) by recognizing comorbidities, electrocardiographic and echocardiographic parameters.

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