Prediction of Disease Progression of ALS based on Machine Learning

DOI

10.2991/978-94-6463-540-9_6How to use a DOI?

Keywords

ALS; MLP; Decision-tree

Abstract

Based on machine learning methods, this paper explores the potential of decision time and Multilayer Perceptron (MLP) in predicting trends in Amyotrophic Lateral Sclerosis(ALS), and introduces a prediction model based on decision trees and multi-layer perceptrons (MLPS). The paper first analyzes the pathological features of ALS as a neurodegenerative disease, its complex pathogenesis, and the data scarcity and treatment challenges facing the medical community. Then, it describes in detail the construction process of the proposed predictive model, including data source acquisition, feature engineering processing, model training, and evaluation methods, as well as the adoption of weighted processing method and the application of decision trees and multilayer perceptron (MLP) in prediction and diagnosis.The results show that there is a certain degree of error in the prediction of random number groups, but with the increase of sample size, the prediction accuracy is gradually improved. Further discussion highlighted new advances in current ALS research, such as genetic and environmental factors’ influence and the application of neuroimaging techniques. Finally, the paper summarizes the findings of the study. It points out that as the accumulation of medical data increases, the accuracy of the predictive model will be further improved to provide more accurate support for the management and treatment of ALS. The significance of this study lies in providing a new approach to ALS prediction and a more precise tool for the medical community to better understand and respond to this serious disease.

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  - Shuzhe Zhang
PY  - 2024
DA  - 2024/10/16
TI  - Prediction of Disease Progression of ALS based on Machine Learning
BT  - Proceedings of the 2024 2nd International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2024)
PB  - Atlantis Press
SP  - 46
EP  - 52
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-540-9_6
DO  - 10.2991/978-94-6463-540-9_6
ID  - Zhang2024
ER  -