Citation

Jayapradha, J. and Haw, Su Cheng and Senthil Kumar, T. and Shandilya, Shubham Kumar and Pradhan, Sambit (2026) NeuroInsight-PD: Early Detection and Classification of Parkinson’s Disease. Advances in Artificial Intelligence and Machine Learning. ISSN 2582-9793

Text 738065352.pdf - Published Version Restricted to Repository staff only Download (8MB)

Abstract

Most people with Parkinson’s Disease are diagnosed too late. By the time a neurologist discovers the condition through standard clinical tests, the brain has already lost 60% or more of its dopamine-producing cells. At this stage, the disease has progressed to a level where slowing its progression becomes challenging. The earliest signs are overlooked and only become apparent during clinical evaluation. There is a gradual decrease in handwriting, along with traces of tremor. Finger dexterity becomes rhythmically inaccurate. Changes in gait patterns are practically unnoticed by the naked eye but can be measured using sensors. The first stage of the NeuroInsight-PD project is devoted to the initial state of the motor dysfunction. The platform records locomotor pattern on a set of wearable sensors, fine motor skill with a series of tremor and finger-tapping tests, and spatial anomalies known of micrographia with a set of handwritten samples. Instead of implementing one of the three modalities, the system combines all three streams of data into the different streams of processing them: a Random Forest classifier is used on the structured sensor data, and an EfficientNetB0 convolutional neural network processes the handwriting images. The scores are then combined as the final results. through the weighted decision-fusion layer, which generates a single likelihood estimate of the Parkinson disease. Experimental analysis shows that the combined system has a 94.6% accuracy and 94.9% precision, which is always higher than the model that is solely dependent on one of the modalities. Ablation analysis indicates that elements such as hard negative mining, threshold calibration, and other elements significantly contribute to the sensitivity of early detection. The solution is deployed to FastAPI back-end and ReactJS front-end offering the ability to screen in real time and be scaled.

Item Type:	Article
Uncontrolled Keywords:	Parkinson’s disease, Early detection, Multimodal data fusion
Subjects:	R Medicine > R Medicine (General) > R858-859.7 Computer applications to medicine. Medical informatics
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	30 Oct 2026 07:37
Last Modified:	30 Oct 2026 07:37
URII:	http://shdl.mmu.edu.my/id/eprint/17058

Downloads

Edit (login required)