Predicting Cardiovascular Disease Risk in Tobacco Users Using Machine Learning Algorithms.

Cardiovascular Diseases (CVDs) present a substantial global health burden, with tobacco use as a major risk factor. While extensive research has identified several risk factors for CVDs, there is a gap in predictive models that account for a combination of clinical factors, lifestyle factors, and other determinants in order to predict CVD risk. In addition, existing studies tend to overlook the interactions among risk factors within high-risk populations, such as tobacco users.

Identifying Features for Keloid Scars Subtyping Using K-Modes Clustering.

Keloids are pathological scars that form due to abnormal wound healing, characterized by an overgrowth of collagen. They develop beyond the sites of the original lesion and have a high recurrence rate, are cosmetically deforming, can cause irritation, physical discomfort, and psychological stress, and frequently impact the quality of life. Despite an array of therapeutic approaches, both surgical and non-surgical, no solutions have been able to offer long-term relief.