Hyper Triglycerides Prognosis Using Machine Learning and Data Science

This study investigates the prognostic potential of machine learning and data science in evaluating hypertriglyceridemia, a condition linked to various health risks. Triglycerides, a substantial component of body fat derived from dietary intake, are intricately associated with these risks. The research explores the use of data science methodologies, especially machine learning algorithms, for precise prognoses. The integration of artificial neural networks and diverse data sources contributes to advancing predictive models, aiming to enhance understanding and improve early detection and preventive interventions for hypertriglyceridemia. This study also emphasizes the role of triglycerides in the body, originating from dietary sources such as butter and oils. Excess calories, not immediately utilized, transform into stored triglycerides, released by the body for energy distribution through VLDL cholesterol particles. Elevated triglyceride levels correlate with increased risks of heart disease, coronary artery disease (CAD), stroke, and chronic conditions affecting the liver and kidneys. The rapid progress in data science, driven by diverse data sources, positions the field among the fastest-evolving. Integration with machine learning technologies significantly improves healthcare solutions, particularly in disease prediction through artificial neural networks. These advancements have a substantial impact on addressing heart disease (4.4 million annual deaths according to the WHF dossier) and cholesterol-related fatalities (estimated at nearly 2.6 million by the WHO).