Title: Risk stratification of cardiovascular disease in type 2 diabetes using LDA and CNN for clinical decision management - a multi-centre study in eastern India

Authors: Suparna Dutta; Saswati Mukherjee; Medha Nag; Sujoy Majumdar; Ghanshyam Goyal

Addresses: Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India ' School of Eduation Technology, Jadavpur University, Kolkata, India ' School of Engineering Entrepreneurship, IIT Kharagpur, India ' GD Hospital & Diabetes Institute, Kolkata, India ' ILS Hospital, Kolkata, India

Abstract: Approximately 72.9 million patients of type 2 diabetes mellitus (T2DM) in India are at a potential risk of cardiovascular diseases (CVDs), strokes and peripheral gangrene. CVD is a major cause of disability and death is one of the major areas of risk severity stratification study. Unlike well-known prediction score models of CVD herein, a unique assessment deep learning model is proposed to stratify the cardiovascular events in different risk grades in T2DM individuals This risk assessment tool can aid clinicians in decision management of CVD risk severity. It is a retrospective cross-sectional observational study that stratifies risks using linear discriminant analysis (LDA) and convolution neural network (CNN). Class separability feature of LDA helps to achieve optimal performance. The model is externally validated in a cohort of 4,719 individuals with T2DM to assess performance heterogeneity across different settings.

Keywords: convolution neural network; CNN; linear discriminant analysis; LDA; support vector machine; SVM; risk stratification; cardiovascular disease; CVD; type 2 diabetes mellitus; T2DM; India.

DOI: 10.1504/IJMEI.2024.135682

International Journal of Medical Engineering and Informatics, 2024 Vol.16 No.1, pp.1 - 14

Received: 13 May 2021
Accepted: 18 Sep 2021
Published online: 22 Dec 2023 *