innovative deep learning algorithms for predicting cardiovascular disease among patients with T2DM

Abstract

Cardiovascular diseases (CVDs) are the leading cause of mortality newlineglobally, underlining the critical need for effective prediction and prevention newlinestrategies. According to the World Health Organization, CVDs are newlineresponsible for an estimated 17.9 million deaths each year, which accounts for newline31% of all global deaths. Of these, over three-quarters occur in low- and newlinemiddle-income countries, emphasizing the global disparity in health outcomes newlineand access to care. Heart attacks and strokes are typically sudden, severe newlineevents mainly brought on by a blockage that stops blood flow to the heart or newlinebrain. Moreover, medical therapy for diabetes, high blood lipids, and newlinehypertension isrequired to lower cardiovascular risk and stop heart attacks and newlinestrokes in those with these disorders. The burden is particularly heavy in newlinepatients with Type 2 Diabetes Mellitus (T2DM), where the risk of developing newlineCVDs is significantly heightened. This study aims to enhance the accuracy newlineand efficiency of CVD risk prediction in T2DM patients using advanced newlineartificial intelligence (AI) methodologies, addressing a vital need in newlinecontemporary healthcare. A common illness known as type 2 diabetes raises newlinethe blood sugar level too high. It may result in symptoms like extreme thirst, newlinefrequent urination, and fatigue. Also, it may make one more susceptible to newlinedeveloping central heart, nerve, and eye issues. Type 2 diabetes mellitus newline(T2DM) is a common metabolic condition that increases the risk of newlinedeveloping atherosclerotic CVD and diabetic cardiomyopathy, which can newlinecause heart failure through several mechanisms, including myocardial newlineinfarction and chronic pressure overload. Recent studies have demonstrated newlinethe potential of machine learning and artificial intelligence in enhancing the newlineaccuracy of CVD risk prediction. These technologies can process vast newlinedatasets, identifying patterns and risk factors that might be overlooked in newlinetraditional analysis. newline newline