Cardiovascular sickness stays a main source of worldwide dreariness and mortality, requiring precise early expectation and counteraction systems. This study proposes an inventive way to deal with improve the effectiveness of CVD expectation utilizing AI calculations combined with two high-level element determination strategies: Alleviation and Rope. The goal is to further develop expectation exactness while limiting the quantity of elements, consequently improving both model execution and interpretability. In this exploration, a thorough dataset containing clinical and segment factors from a different populace was gathered. The Alleviation highlight choice procedure was at first utilized to distinguish and rank the most important elements in view of their effect on forecast exactness. Accordingly, the Rope strategy was utilized to additionally refine the list of capabilities by punishing non-contributory highlights.