Multi-Modal Framework for Transformative Paralysis Agitans Approach

The diagnosis of Parkinson's Disease (PD), also called Paralysis Agitans (PA), is based on subjective clinical assessments and a single-modality data, being affected by the problems signal are late to arrive or as we used to say there is no objects for creating specific and anomalous parameter values. PSO (Particle Swarm Optimization) harmonized with the social behaviour of birds. Advancing ML to improve diagnostic accuracy by identifying hidden patterns, reducing bias, distinguished diagnoses and early detection to address these deficiencies, we present the Multi-Modal Fusion Framework (MMFF), which passes clinical, motor, neuroimaging and genetic data through a computationally efficient multiple-modality operation that capitalizes on state-of-the-art ML techniques. By combining ensemble learning, multimodal analysis and transfer learning MMFF improves robustness and interpretability. The model based on artificial intelligence has been shown to not only perform better than existing methods but also offers clinicians a new method of diagnosis that is guided by data.