Version 1
: Received: 18 March 2020 / Approved: 19 March 2020 / Online: 19 March 2020 (10:52:31 CET)
How to cite:
Abdullah Farid, A.; Selim, G.; Khater, H. Applying Artificial Intelligence Techniques to Improve Clinical Diagnosis of Alzheimer’s Disease. Preprints2020, 2020030297. https://doi.org/10.20944/preprints202003.0297.v1
Abdullah Farid, A.; Selim, G.; Khater, H. Applying Artificial Intelligence Techniques to Improve Clinical Diagnosis of Alzheimer’s Disease. Preprints 2020, 2020030297. https://doi.org/10.20944/preprints202003.0297.v1
Abdullah Farid, A.; Selim, G.; Khater, H. Applying Artificial Intelligence Techniques to Improve Clinical Diagnosis of Alzheimer’s Disease. Preprints2020, 2020030297. https://doi.org/10.20944/preprints202003.0297.v1
APA Style
Abdullah Farid, A., Selim, G., & Khater, H. (2020). Applying Artificial Intelligence Techniques to Improve Clinical Diagnosis of Alzheimer’s Disease. Preprints. https://doi.org/10.20944/preprints202003.0297.v1
Chicago/Turabian Style
Abdullah Farid, A., Gamal Selim and Hatem Khater. 2020 "Applying Artificial Intelligence Techniques to Improve Clinical Diagnosis of Alzheimer’s Disease" Preprints. https://doi.org/10.20944/preprints202003.0297.v1
Abstract
Alzheimer's disease (AD) is a significant regular type of dementia that causes damage in brain cells. Early detection of AD acting as an essential role in global health care due to misdiagnosis and sharing many clinical sets with other types of dementia, and costly monitoring the progression of the disease over time by magnetic reasoning imaging (MRI) with consideration of human error in manual reading. Our proposed model, in the first stage, apply the medical dataset to a composite hybrid feature selection (CHFS) to extract new features for select the best features to improve the performance of the classification process due to eliminating obscures features. In the second stage, we applied a dataset to a stacked hybrid classification system to combine Jrip and random forest classifiers with six model evaluations as meta-classifier individually to improve the prediction of clinical diagnosis. All experiments conducted on a laptop with an Intel Core i7- 8750H CPU at 2.2 GHz and 16 G of ram running on windows 10 (64 bits). The dataset evaluated using an explorer set of weka data mining software for the analysis purpose. The experimental show that the proposed model of (CHFS) feature extraction performs better than principal component analysis (PCA), and lead to effectively reduced the false-negative rate with a relatively high overall accuracy with support vector machine (SVM) as meta-classifier of 96.50% compared to 68.83% which is considerably better than the previous state-of-the-art result. The receiver operating characteristic (ROC) curve was equal to 95.5%. Also, the experiment on MRI images Kaggle dataset of CNN classification process with 80.21% accuracy result. The results of the proposed model show an accurate classify Alzheimer's clinical samples against MRI neuroimaging for diagnoses AD at a low cost.
Keywords
Data Mining; Alzheimer’s Dementia; Composite Hybrid Feature Selection; Machine learning; Stack Hybrid Classification; AI Techniques; Classification; AD Diagnose; Clinical AD Dataset
Subject
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
