Machine Learning (ML) techniques have become increasingly crucial in healthcare applications, where model transparency and real-time decision-making are paramount. While complex ML models offer powerful capabilities, lightweight approaches provide more efficient and resource-friendly solutions, making them ideal for deployment on resource-constrained wearable devices in health monitoring and diagnostics. Furthermore, the interpretability of these models ensures that healthcare professionals can trust and comprehend the ML-driven insights, fostering stronger collaborations across various aspects of healthcare, from diagnosis and treatment to patient care. By prioritizing both simplicity and interpretability, these ML solutions hold great promise for enhancing the accessibility, efficiency, and effectiveness of healthcare delivery. As the healthcare industry continues to evolve, the integration of such user-friendly and transparent ML models could significantly contribute to improved patient outcomes, more informed decision-making by healthcare providers, and ultimately, a more responsive and personalized approach to medicine.