Applying Artificial Intelligence Techniques for Prediction of Neurodegenerative Disorders: A Comparative Case-Study on Clinical Tests and Neuroimaging Tests with Alzheimer’s Disease

Applying Artificial Intelligence Techniques for Prediction of Neurodegenerative Disorders: A Comparative Case-Study on Clinical Tests and Neuroimaging Tests with Alzheimer’s Disease

    Proceedings of ‏The 2nd International Conference on Advanced Research in Applied Science and Engineering

    Year: 2020

    DOI:

    [Fulltex PDF]

    Applying Artificial Intelligence Techniques for Prediction of Neurodegenerative Disorders: A Comparative Case-Study on Clinical Tests and Neuroimaging Tests with Alzheimer’s Disease

    Ahmed Abdullah Farid, Gamal Ibrahim Selim and Hatem Awad A. Khater

     

    ABSTRACT: 

    Alzheimer’s disease (AD) detection 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. This paper goal a comparative study on the performance of data mining techniques on two datasets of Clinical and Neuroimaging Tests with AD. Our ‎proposed model in the first stage, apply clinical medical dataset to a composite hybrid feature selection (CHFS), for extract new features to select the best features due to ‎eliminating obscures features, In parallel with Apply a novel hybrid feature extraction of three batch edge detection algorithm and texture from MRI images dataset and optimized with fuzzy 64-bin histogram. In the second stage, we applied a clinical dataset to a stacked ‎hybrid classification(SHC) model to combine Jrip and random forest ‎classifiers with six model evaluations as meta-classifier individually ‎to improve the prediction of clinical diagnosis. At the same stage of improving the classification accuracy of neuroimaging (MRI) dataset images by applying a convolution neural network (CNN) in comparison with traditional classifiers, running on extracted features from images. The authors have collected the clinical dataset of 426 subjects with (1229 ‎potential patient sample) from oasis.org and (MRI) dataset from a benchmark kaggle.com with a total of around ~5000 images each segregated into the severity of Alzheimer’s. The datasets 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 ‏ lead to ‎ effectively reduced the false-negative ‎rate with a ‎relatively ‎high overall accuracy with a stack hybrid classification of support vector machine (SVM) as meta-classifier of 96.50% compared to 68.83% of the previous result on a clinical dataset, Besides a compared model of CNN classification on MRI images dataset of 80.21%. The results showed the superiority of our CHFS model in predicting Alzheimer’s disease more accurately with the clinical medical dataset in early-stage compared with the neuroimaging (MRI) dataset. The results of the proposed model were able to predict with accurately classify ‎Alzheimer’s clinical samples at a low cost in comparison with the MRI-CNN images model‎ at the early stage and get a good indicator for high classification rate for MRI images when applying our proposed model of SHC.

    Keywords: Data Mining, Alzheimer’s Dementia, Composite Hybrid Feature Selection, Machine learning, stack ‎Hybrid Classification, AI, MRI, Neuroimaging, MPEG7 edge histogram feature extraction, CNN.