Detecting Parkinson’s Disease Through Handwriting Patterns

Reshma R. Vernekar, Sammed B. Ghattad and Ms Sheetal S. Bandekar
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Reshma R. Vernekar: Department of Master of Computer Applications (MCA), KLS Gogte Institute of Technology, Belagavi. Belagavi, India
Sammed B. Ghattad: Department of Master of Computer Applications (MCA), KLS Gogte Institute of Technology, Belagavi. Belagavi, India
Ms Sheetal S. Bandekar: Department of Master of Computer Applications (MCA), KLS Gogte Institute of Technology, Belagavi. Belagavi, India

International Journal of Research and Innovation in Applied Science, 2025, vol. 10, issue 7, 916-927

Abstract: Parkinson’s disease (PD) is a gradually worsening neurological disorder that mainly affects motor functions due to the decline of dopamine-producing neurons in the substantia nigra. Early and precise diagnosis is often difficult because traditional tools like MRI, PET scans, or neurological tests tend to be costly, subjective, and not widely available. Handwriting analysis has emerged as a non-invasive, cost-efficient biomarker, capable of revealing early-stage motor abnormalities such as micrographia, tremors, and bradykinesia. This literature survey systematically reviews recent advancements in the automatic detection of PD using handwriting patterns, leveraging machine learning (ML) and deep learning (DL) algorithms. It highlights methodologies involving Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), hybrid CNN-RNN models, and transfer learning approaches applied to both static images and dynamic time-series handwriting data. The review also explores data preprocessing strategies, augmentation techniques, and handcrafted as well as learned feature extraction methods. Studies report diagnostic accuracies often exceeding 90%, with some achieving over 98% using optimized architectures. Explainable Artificial Intelligence (XAI) frameworks, such as LIME, have further improved clinical trust in model predictions. Despite these achievements, challenges remain in data diversity, generalizability, and deployment on low-power edge devices, prompting the need for future research focused on scalable and interpretable diagnostic systems.

Date: 2025
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