Machine Learning and Deep Learning for Parkinson's Disease Detection: A Comprehensive Review

Surveyed state-of-the-art machine learning and deep learning techniques for Parkinson’s disease detection using speech, gait, handwriting, and biomedical signals. The review compares traditional ML pipelines with modern multimodal deep learning frameworks, highlighting challenges in interpretability, dataset imbalance, and real-world clinical deployment.

Research Paper

AI in healthcare

Project Overview

Under Review | Springer

Focus Areas

Healthcare AI · Machine Learning · Deep Learning · Multimodal Learning · Biomedical Signal Analysis · Explainable AI

Conducted a comprehensive research review on machine learning and deep learning approaches for Parkinson’s disease detection using speech, gait, handwriting, wearable sensors, and biomedical signals. Traditional diagnostic procedures are often subjective, expensive, and time-intensive, so the review explored how AI-driven systems enable earlier, non-invasive, and data-driven neurological assessment.


Research Scope

Analyzed and compared traditional machine learning pipelines with modern deep learning architectures for Parkinson’s disease screening, multimodal biomedical analysis, and predictive healthcare intelligence.

Key Contributions

  • Reviewed machine learning and deep learning frameworks for Parkinson’s disease detection using multimodal biomedical data

  • Analyzed AI-based diagnostic systems leveraging speech processing, gait analysis, handwriting recognition, wearable sensors, and physiological biomarkers

  • Compared traditional ML models with CNNs, RNNs, transformers, hybrid architectures, and multimodal fusion systems

  • Investigated challenges related to interpretability, dataset imbalance, model generalization, and clinical deployment readiness

  • Explored emerging research trends in explainable AI, multimodal learning, and predictive healthcare systems

  • Structured findings into a comparative analytical framework highlighting strengths, limitations, and future directions of existing diagnostic approaches

Outcome

The review demonstrates how machine learning and deep learning techniques are transforming Parkinson’s disease detection through non-invasive and data-driven methodologies. It highlights the growing importance of multimodal healthcare AI systems while identifying key challenges related to robustness, interpretability, scalability, and real-world clinical integration.

Tech Stack

Machine Learning · Deep Learning · Healthcare AI · Biomedical Signal Analysis · Multimodal Learning · Explainable AI · Research Analysis · Predictive Healthcare Systems


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