Timely and accurate classification of cardiac valvular diseases is imperative for effective therapeutic interventions and improved health care applications. This research introduces a hand-crafted feature based solution for classifying cardiac valvular diseases using phonocardiogram (PCG) signals, achieving an accuracy of 99.5%. Instead of relying on computationally intensive deep learning paradigms, our approach emphasizes statistical feature extraction from raw sound wave data. By studying the impact of factors like segment lengths, model selection, and the nature of feature extraction, we have proposed an efficient alternative for cardiovascular diseases diagnostics. Our methodology, when positioned against benchmark deep learning solutions, performs remarkably. This holds significant advantages for real-world applications where computational efficiency and model lightweightness are crucial.