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Predicting Alzheimer's Disease Using Filter Feature Selection Method | ||
| IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING | ||
| Article 2, Volume 22, Issue 4, December 2022, Pages 13-27 PDF (790.65 K) | ||
| Document Type: Research Paper | ||
| DOI: 10.33103/uot.ijccce.22.4.2 | ||
| Authors | ||
| Shaymaa Taha Ahmed* 1; Suhad Malallah Kadhem2 | ||
| 1Department of Computer Science, College of Basic Education, University of Diyala, Diyala, Iraq | ||
| 2Computer Sciences Department, University of Technology, Baghdad, Iraq | ||
| Abstract | ||
| Alzheimer’s disease (AD) is caused by multiple variables. Alzheimer's disease development and progression are influenced by genetic variants. The molecular pathways causing Alzheimer's disease are still poorly understood. In Alzheimer's disease research, determining an effective and reliable diagnosis remains a major difficulty, particularly in the early stages (i.e., Moderate Cognitive Impairment (MCI)). Researchers and technologists working in the fields of machine learning and data mining can help improve the situation, early AD diagnosis but face a hurdle when it comes to high- dimensional data processing. By reducing irrelevant and redundant data from microarray gene expression data, the technique of feature selection can save computing time, improve learning accuracy, and encourage a deeper effect on the learning system or data. The feature selection strategy described in this article reduces data noise well. In particular, Pearson's correlation coefficient is used to assess data redundancy. The efficacy of these features is assessed using the Support Vector Machine (SVM) classification approach. The proposed approach has an accuracy of up to 91.1 %. As a result, newly established approaches for early diagnosis of Alzheimer's disease(AD) are being improved. | ||
| Keywords | ||
| Alzheimer’s Disease; Support vector machine; machine learning; feature selection; Pearson’s correlation coefficient | ||
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