Optimized Machine Learning Model for Disease Prediction and Treatment Recommendation Through Gait Analysis

This research introduces an advanced machine learning model for disease prediction and treatment recommendations, leveraging gait analysis as a primary input. Using a diverse dataset encompassing various health conditions, the model employs state-of-the-art feature extraction and optimization techniques to accurately predict underlying health conditions. Beyond disease prediction, it incorporates a treatment recommendation component, generating personalized suggestions based on gait analysis. Rigorous testing demonstrates the model's robustness and significant improvements in disease prediction accuracy and treatment recommendation precision compared to existing approaches. In conclusion, this cutting-edge model enhances early intervention and patient outcomes through comprehensive gait analysis for disease prediction and personalized treatment recommendations. Overall, our work underscores the immense potential of optimized machine learning models in harnessing the power of gait analysis for disease prediction and treatment recommendation. By bridging the gap between data-driven insights and clinical decision-making, we aim to empower healthcare professionals with innovative tools for enhancing patient care and improving health outcomes.