Explainable Artificial Intelligence for Diagnosis of Cardiovascular Disease

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Author(s): Megha Bhushan (University of Seville, Spain), Abhishek Kukreti (DIT University, India)and Arun Negi (Deloitte USI, India)
Copyright: 2024
Pages: 10
Source title: Improving Security, Privacy, and Connectivity Among Telemedicine Platforms
Source Author(s)/Editor(s): Nuno Geada (ISCTE, University Institute of Lisbon, Portugal)
DOI: 10.4018/979-8-3693-2141-6.ch007

Keywords: Knowledge Management and Library Science / Medical Information Science Reference / Medical Technologies / Telemedicine

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Abstract

Cardiovascular disease (CVD) is among the top causes of mortality in today's world; according to the World Health Organisation (WHO), 17.9 million individuals worldwide have died from this illness, leading to 31% of all fatalities. Through early detection and alteration in lifestyle, more than 80% of deaths due to CVD can be avoided. The majority of CVD cases are identified in adults; however, the risk factors for its beginning develops at a younger age. Various machine learning and deep learning algorithms have been utilized to diagnose and predict different types of CVDs, resulting in the development of sophisticated and efficient risk classification algorithms for every patient with CVD. These models incorporate explainability modalities which can improve people's comprehension of how reasoning works, increase transparency, and boost confidence in the usage of models in medical practice. It can help in optimising the frequency of doctor visits and carrying out prompt therapeutic along with preventative interventions against CVD occurrences.

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Explainable Artificial Intelligence for Diagnosis of Cardiovascular Disease

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