Modified Federated Learning for Parkinson's Disease Prediction
Received: 28 October 2025 | Revised: 22 January 2026 | Accepted: 28 January 2026 | Online: 4 April 2026
Corresponding author: Muhammad Amir Khan
Abstract
Parkinson's Disease (PD) is a common neurodegenerative condition characterized by tremors, stiffness, and bradykinesia. Early PD detection and prediction are critical for optimizing treatment regimens and improving patient outcomes. This research investigates the efficacy of machine learning algorithms in PD diagnosis and progression prediction. A review of relevant literature was conducted, including research that used various datasets and machine learning algorithms. These algorithms include Support Vector Machines (SVM), Naive Bayes (NB), K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), Decision Trees (DT), Random Forest (RF), Expectation-Maximization (EM), Principal Component Analysis (PCA), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Support Vector Regression (SVR), and Attention-based Deep Convolutional Neural Networks (ADCNN). The results demonstrate the potential of machine learning techniques in enhancing PD diagnosis and prediction accuracy. Feature selection, model optimization, and integration of multiple modalities, such as genetic, imaging, and clinical data, have shown promise in improving model performance. The findings suggest that machine learning can complement traditional diagnostic methods and aid in early detection and personalized treatment planning for patients with PD. Future research should focus on developing explainable Artificial Intelligence (AI) models and conducting large-scale longitudinal studies to validate the performance and generalizability of machine learning models in real-world clinical settings. By addressing these challenges, machine learning can significantly contribute to advancing PD research and improving patient care.
Keywords:
federated learning, Parkinson's Disease (PD), prediction, data preprocessing, K-Nearest Neighbors (KNN), data privacy, aggregationDownloads
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Copyright (c) 2026 Sonia Ghazal, Razia Manan, Ali Khalid, Mohammad Shahid, Umar Farooq Khattak, Muhammad Amir Khan
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