Classification and analysis of chronic health conditions influencing dementia progression using machine learning methods
1 College of Medicine, Isabela State University, Echague Campus, Echague, Isabela, Philippines.
2 School of Information Technology, MAPUA University, Makati Campus, Makati, Philippines.
3 College of Computing Studies, Information and Communication Technology, Isabela State University, Echague Campus, Echague, Philippines.
4 University of Lasalette College, Santiago City, Philippines.
5 Department of Information Technology, Northeaster College, Santiago City, Philippines.
Research Article
World Journal of Advanced Research and Reviews, 2024, 24(03), 1600–1608
Article DOI: 10.30574/wjarr.2024.24.3.3848
Publication history:
Received on 07 November 2024; revised on 14 December 2024; accepted on 17 December 2024
Abstract:
Dementia, a progressive neurodegenerative condition, poses significant challenges to global healthcare systems due to its rising prevalence and complexity in diagnosis. This study employs a machine learning-based methodology to categorize dementia-related data and forecast disease progression, enabling early diagnosis and personalized treatment planning. Using a structured dataset comprising 24 features and 2230 entries, data preprocessing was conducted to address missing values, encode categorical variables, and normalize numerical features. Exploratory data analysis revealed key insights, including a strong inverse correlation between cognitive test scores and dementia severity. A Random Forest Classifier was trained to predict dementia presence, achieving an accuracy of 91.40%. Feature importance analysis identified "Cognitive_Test_Scores," "Prescription_Memantine," and "Dosage in mg" as the most critical predictors, aligning with clinical evidence. The confusion matrix highlighted high predictive accuracy but revealed misclassifications, particularly false negatives, which are critical in medical contexts. This study underscores the potential of machine learning in dementia diagnosis and management. By identifying significant predictors and achieving robust classification performance, the research provides a foundation for integrating artificial intelligence into healthcare systems to enhance diagnostic accuracy, optimize treatment plans, and improve patient outcomes. Further refinement and validation using larger datasets are recommended for broader applicability in clinical settings.
Keywords:
Dementia; Correlation; Random Forest; Machine Learning; Medical AI
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0