Parkinson’s Disease Detection Through Spiral Drawing Recognition Using Machine Learning Approach

DOI

10.2991/978-94-6463-589-8_31How to use a DOI?

Keywords

Parkinson’s Disease; Machine Learning; ResNet50; Random Forest; Spiral Drawing

Abstract

Parkinson’s Disease (PD) is a neurodegenerative disorder characterized by motor impairments, and early detection is crucial for effective intervention. PD patient diagnose the disease only after having serious symptoms and the diagnosis requires huge amount of time. The self-serves diagnostic can be applied with the help of computer aided technology. This paper aims to identify the features of Parkinson's disease symptoms by assessing on the spiral drawing images and develop a Parkinson's disease prediction system using machine learning model. By leveraging the power of machine learning algorithms, ResNet50 and Random Forest with Histogram Oriented Gradient (HOG) models are trained on spiral and waves imaging datasets comprising spiral drawings of healthy individuals and with PD symptoms. Data are pre-processed. After the data is cleaned, the models were trained with different experiments for ResNet50 model, also random state for Random Forest with HOG and to be compared to find the best model with highest accuracy and lowest loss. As the results, the best model is ResNet50 model with 50 epoch and 32 batch size as it has the highest validation accuracy (0.8967) and lowest validation loss (0.245). The ResNet50 Model 2 is chosen to be embedded in the PD detection system, which is being developed using Python. The model can be enhanced further in the future by increasing the number of features on detecting spiral drawings such as speed and pressure of pen when drawing.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Nur Atiqah Sia Abdullah
AU  - Farahnatasyah Abdul Hanan
AU  - Abdul Hakim Abdul Rahman
PY  - 2024
DA  - 2024/12/01
TI  - Parkinson’s Disease Detection Through Spiral Drawing Recognition Using Machine Learning Approach
BT  - Proceedings of the International Conference on Innovation & Entrepreneurship in Computing, Engineering & Science Education (InvENT 2024)
PB  - Atlantis Press
SP  - 341
EP  - 351
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-589-8_31
DO  - 10.2991/978-94-6463-589-8_31
ID  - Abdullah2024
ER  -