An Ensemble-Learning Based Application to Predict the Earlier Stages of Alzheimer’s Disease (AD)
The fact that ensemble methods enhance the prediction performance. Therefore, we focused on developing a weighted ensemble method using a novel combination of Cerebrospinal Fluid (CSF) protein biomarkers to predict AD's earlier stages with greater accuracy than the state-of-the-art CSF protein...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9288747/ |
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| author | Asif Hassan Syed Tabrej Khan Atif Hassan Nashwan A. Alromema Muhammad Binsawad Alhuseen Omar Alsayed |
| author_facet | Asif Hassan Syed Tabrej Khan Atif Hassan Nashwan A. Alromema Muhammad Binsawad Alhuseen Omar Alsayed |
| author_sort | Asif Hassan Syed |
| collection | DOAJ |
| description | The fact that ensemble methods enhance the prediction performance. Therefore, we focused on developing a weighted ensemble method using a novel combination of Cerebrospinal Fluid (CSF) protein biomarkers to predict AD's earlier stages with greater accuracy than the state-of-the-art CSF protein biomarkers. In this regard, two feature selection methods, namely the Recursive Feature Elimination (RFE) and L1 regularization method were used to screen the most important subset of features for building a classification model using the Mild Cognitive Impairment (MCI) dataset. A novel combination of three biomarkers, namely Cystatin C, Matrix metalloproteinases (MMP10), and tau protein, was screened using the linear Support Vector Machine (SVM) and Logistic Regression (LR) classifier based RFE method. Two-tailed unpaired t-test analysis at a 5% significance level showed a significant difference between the mean levels of Cystatin C, MMP10, and tau protein between cognitive normal and cognitively impaired groups. An ensemble model using a weighted average of two best performing classifiers (LR and Linear SVM) was created using a novel subset of three most informative features. Our ensemble model's weighted average results performed significantly better than LR and Linear SVM base classifiers' performance. The Receiver Operating Characteristic Curve (ROC_AUC) and Area under Precision-Recall values (AUPR) of our proposed model were observed to be 0.9799 ± 0.055 0.9108 ± 0.015, respectively. The performance of our proposed weighted averaged ensemble model built using a novel combination of CSF protein biomarkers was significantly better (p <; 0.001) than models generated using different combinations of CSF protein biomarkers obtained from recent studies. An ensemble-learning based application was implemented and deployed at Heroku at https://appsalzheimer.herokuapp.com. |
| first_indexed | 2024-12-17T05:02:28Z |
| format | Article |
| id | doaj.art-e5b9997185a44136a718a0a2770c1113 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T05:02:28Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e5b9997185a44136a718a0a2770c11132022-12-21T22:02:31ZengIEEEIEEE Access2169-35362020-01-01822212622214310.1109/ACCESS.2020.30437159288747An Ensemble-Learning Based Application to Predict the Earlier Stages of Alzheimer’s Disease (AD)Asif Hassan Syed0https://orcid.org/0000-0002-2699-9102Tabrej Khan1https://orcid.org/0000-0002-9510-8537Atif Hassan2Nashwan A. Alromema3https://orcid.org/0000-0001-6208-2863Muhammad Binsawad4https://orcid.org/0000-0003-0915-7058Alhuseen Omar Alsayed5https://orcid.org/0000-0003-3044-799XDepartment of Computer Science, Faculty of Computing and Information Technology in Rabigh (FCITR), King Abdulaziz University, Jeddah, Saudi ArabiaDepartment of Information Systems, Faculty of Computing and Information Technology in Rabigh (FCITR), King Abdulaziz University, Jeddah, Saudi ArabiaDepartment of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, IndiaDepartment of Computer Science, Faculty of Computing and Information Technology in Rabigh (FCITR), King Abdulaziz University, Jeddah, Saudi ArabiaDepartment of Computer Science, Faculty of Computing and Information Technology in Rabigh (FCITR), King Abdulaziz University, Jeddah, Saudi ArabiaScientific Researcher, Deanship of Scientific Research, King Abdulaziz University, Jeddah, Saudi ArabiaThe fact that ensemble methods enhance the prediction performance. Therefore, we focused on developing a weighted ensemble method using a novel combination of Cerebrospinal Fluid (CSF) protein biomarkers to predict AD's earlier stages with greater accuracy than the state-of-the-art CSF protein biomarkers. In this regard, two feature selection methods, namely the Recursive Feature Elimination (RFE) and L1 regularization method were used to screen the most important subset of features for building a classification model using the Mild Cognitive Impairment (MCI) dataset. A novel combination of three biomarkers, namely Cystatin C, Matrix metalloproteinases (MMP10), and tau protein, was screened using the linear Support Vector Machine (SVM) and Logistic Regression (LR) classifier based RFE method. Two-tailed unpaired t-test analysis at a 5% significance level showed a significant difference between the mean levels of Cystatin C, MMP10, and tau protein between cognitive normal and cognitively impaired groups. An ensemble model using a weighted average of two best performing classifiers (LR and Linear SVM) was created using a novel subset of three most informative features. Our ensemble model's weighted average results performed significantly better than LR and Linear SVM base classifiers' performance. The Receiver Operating Characteristic Curve (ROC_AUC) and Area under Precision-Recall values (AUPR) of our proposed model were observed to be 0.9799 ± 0.055 0.9108 ± 0.015, respectively. The performance of our proposed weighted averaged ensemble model built using a novel combination of CSF protein biomarkers was significantly better (p <; 0.001) than models generated using different combinations of CSF protein biomarkers obtained from recent studies. An ensemble-learning based application was implemented and deployed at Heroku at https://appsalzheimer.herokuapp.com.https://ieeexplore.ieee.org/document/9288747/Mild cognitive impairment (MCI)cerebrospinal fluid (CSF) protein biomarkersclassification modelonline prediction system |
| spellingShingle | Asif Hassan Syed Tabrej Khan Atif Hassan Nashwan A. Alromema Muhammad Binsawad Alhuseen Omar Alsayed An Ensemble-Learning Based Application to Predict the Earlier Stages of Alzheimer’s Disease (AD) IEEE Access Mild cognitive impairment (MCI) cerebrospinal fluid (CSF) protein biomarkers classification model online prediction system |
| title | An Ensemble-Learning Based Application to Predict the Earlier Stages of Alzheimer’s Disease (AD) |
| title_full | An Ensemble-Learning Based Application to Predict the Earlier Stages of Alzheimer’s Disease (AD) |
| title_fullStr | An Ensemble-Learning Based Application to Predict the Earlier Stages of Alzheimer’s Disease (AD) |
| title_full_unstemmed | An Ensemble-Learning Based Application to Predict the Earlier Stages of Alzheimer’s Disease (AD) |
| title_short | An Ensemble-Learning Based Application to Predict the Earlier Stages of Alzheimer’s Disease (AD) |
| title_sort | ensemble learning based application to predict the earlier stages of alzheimer x2019 s disease ad |
| topic | Mild cognitive impairment (MCI) cerebrospinal fluid (CSF) protein biomarkers classification model online prediction system |
| url | https://ieeexplore.ieee.org/document/9288747/ |
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