Atherosclerosis, a chronic inflammatory disease characterized by the accumulation of plaque in arteries, remains a significant global health concern. Early detection and intervention are crucial to prevent its progression and associated complications such as heart attacks and strokes.
Our primary objective is to develop an artificial intelligence-driven predictive model that leverages a range of behavioral features, such as physical activity, smoking habits, and diet, along with important biological markers like blood pressure and blood biochemistry, to accurately predict the likelihood of atherosclerosis development in individuals.
By utilizing a dataset comprising anonymized health records, lifestyle surveys, and medical test results, specifically sourced from the UK Biobank dataset, we aim to identify patterns and correlations that can be used to create an effective predictive model. The UK Biobank dataset provides a rich and diverse source of data from a large cohort of participants, enabling comprehensive analysis and robust predictions for atherosclerosis risk assessment.