EXPLORING DATA MINING APPROACHES FOR EARLY ALZHEIMER'S DETECTION: A COMPREHENSIVE SURVEY
##plugins.themes.bootstrap3.article.main##
Abstract
This literature review addresses the burgeoning global health challenge posed by Alzheimer's disease (AD) and the imperative for early detection. With the gradual onset and the lack of definitive early diagnostic tools, integrating data mining techniques offers a promising avenue for uncovering intricate patterns within diverse datasets. Examining research across neuroimaging, genomics, and clinical records, this survey explores integrated data mining approaches, spanning machine learning, statistical analysis, and artificial intelligence. The review critically evaluates methodologies, emphasizing the potential of collaborative data integration initiatives to develop robust models for early Alzheimer's detection.
##plugins.themes.bootstrap3.article.details##
Section
Articles
References
[1]. N. Tasnim, M. K. Paul and A. H. M. S. Sattar, "Identification of Drop Out Students Using Educational Data Mining," 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), Cox'sBazar, Bangladesh, 2019, pp. 1-5, doi: 10.1109/ECACE.2019.8679385.
[2]. P. Nguyen and C. Vo, "Early In-trouble Student Identification Based on Temporal Educational Data Clustering," 2019 International Conference on Information Technology (ICIT), Bhubaneswar, India, 2019, pp. 313-318, doi: 10.1109/ICIT48102.2019.00062.
[3]. G. Hemanth, M. Janardhan and L. Sujihelen, "Design and Implementing Brain Tumor Detection Using Machine Learning Approach," 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India, 2019, pp. 1289-1294, doi: 10.1109/ICOEI.2019.8862553.
[4]. N. Mathew, "A Boosting Approach for Maternal Hypertensive Disorder Detection," 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), Coimbatore, India, 2018, pp. 1474-1477, doi: 10.1109/ICICCT.2018.8473236.
[5]. Z. Ghasemi, S. Banitaan and G. Al-Refai, "Automated Chagas Disease Vectors Identification using Data Mining Techniques," 2020 IEEE International Conference on Electro Information Technology (EIT), Chicago, IL, USA, 2020, pp. 540-545, doi: 10.1109/EIT48999.2020.9208261.
[6]. M. N. Elagamy, C. Stanier and B. Sharp, "Stock market random forest-text mining system mining critical indicators of stock market movements," 2018 2nd International Conference on Natural Language and Speech Processing (ICNLSP), Algiers, Algeria, 2018, pp. 1-8, doi: 10.1109/ICNLSP.2018.8374370.
[7]. R. G. Franklin and B. Muthukumar, "Survey of Heart Disease Prediction and Identification using Machine Learning Approaches," 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), Thoothukudi, India, 2020, pp. 553-557, doi: 10.1109/ICISS49785.2020.9316119.
[8]. S. Andleeb, R. Ahmed, Z. Ahmed and M. Kanwal, "Identification and Classification of Cybercrimes using Text Mining Technique," 2019 International Conference on Frontiers of Information Technology (FIT), Islamabad, Pakistan, 2019, pp. 227-2275, doi: 10.1109/FIT47737.2019.00050.
[9]. P. B. Chanda and S. K. Sarkar, "Cardiac MR Images Segmentation For Identification Of Cardiac Diseases Using Fuzzy Based Approach," 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India, 2020, pp. 1238-1246, doi: 10.1109/ICSSIT48917.2020.9214080.
[10]. S. De Silva et al., "A Rule-Based System for ADHD Identification using Eye Movement Data," 2019 Moratuwa Engineering Research Conference (MERCon), Moratuwa, Sri Lanka, 2019, pp. 538-543, doi: 10.1109/MERCon.2019.8818865.
[11]. F. Pacheco, E. Exposito, J. Aguilar, M. Gineste and C. Baudoin, "A novel statistical based feature extraction approach for the inner-class feature estimation using linear regression," 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil, 2018, pp. 1-8, doi: 10.1109/IJCNN.2018.8488992.
[12]. Teske, R. Falcon, R. Abielmona and E. Petriu, "Automatic Identification of Maritime Incidents from Unstructured Articles," 2018 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA), Boston, MA, USA, 2018, pp. 42-48, doi: 10.1109/COGSIMA.2018.8423975.
[13]. Aldallal and A. A. A. Al-Moosa, "Using Data Mining Techniques to Predict Diabetes and Heart Diseases," 2018 4th International Conference on Frontiers of Signal Processing (ICFSP), Poitiers, France, 2018, pp. 150-154, doi: 10.1109/ICFSP.2018.8552051.
[14]. X. Zhai et al., "Research on the Biological Basis of Treating Different Diseases with Same Method Based on Big Data Mining and Complex Network," 2018 IEEE 4th International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing, (HPSC) and IEEE International Conference on Intelligent Data and Security (IDS), Omaha, NE, USA, 2018, pp. 264-269, doi: 10.1109/BDS/HPSC/IDS18.2018.00063.
[15]. S. K. K. L, N. K. G and M. J. A, "Coronary Artery Disease Prediction using Data Mining Techniques," 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), Thoothukudi, India, 2020, pp. 693-697, doi: 10.1109/ICISS49785.2020.9316014.
[2]. P. Nguyen and C. Vo, "Early In-trouble Student Identification Based on Temporal Educational Data Clustering," 2019 International Conference on Information Technology (ICIT), Bhubaneswar, India, 2019, pp. 313-318, doi: 10.1109/ICIT48102.2019.00062.
[3]. G. Hemanth, M. Janardhan and L. Sujihelen, "Design and Implementing Brain Tumor Detection Using Machine Learning Approach," 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India, 2019, pp. 1289-1294, doi: 10.1109/ICOEI.2019.8862553.
[4]. N. Mathew, "A Boosting Approach for Maternal Hypertensive Disorder Detection," 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), Coimbatore, India, 2018, pp. 1474-1477, doi: 10.1109/ICICCT.2018.8473236.
[5]. Z. Ghasemi, S. Banitaan and G. Al-Refai, "Automated Chagas Disease Vectors Identification using Data Mining Techniques," 2020 IEEE International Conference on Electro Information Technology (EIT), Chicago, IL, USA, 2020, pp. 540-545, doi: 10.1109/EIT48999.2020.9208261.
[6]. M. N. Elagamy, C. Stanier and B. Sharp, "Stock market random forest-text mining system mining critical indicators of stock market movements," 2018 2nd International Conference on Natural Language and Speech Processing (ICNLSP), Algiers, Algeria, 2018, pp. 1-8, doi: 10.1109/ICNLSP.2018.8374370.
[7]. R. G. Franklin and B. Muthukumar, "Survey of Heart Disease Prediction and Identification using Machine Learning Approaches," 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), Thoothukudi, India, 2020, pp. 553-557, doi: 10.1109/ICISS49785.2020.9316119.
[8]. S. Andleeb, R. Ahmed, Z. Ahmed and M. Kanwal, "Identification and Classification of Cybercrimes using Text Mining Technique," 2019 International Conference on Frontiers of Information Technology (FIT), Islamabad, Pakistan, 2019, pp. 227-2275, doi: 10.1109/FIT47737.2019.00050.
[9]. P. B. Chanda and S. K. Sarkar, "Cardiac MR Images Segmentation For Identification Of Cardiac Diseases Using Fuzzy Based Approach," 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India, 2020, pp. 1238-1246, doi: 10.1109/ICSSIT48917.2020.9214080.
[10]. S. De Silva et al., "A Rule-Based System for ADHD Identification using Eye Movement Data," 2019 Moratuwa Engineering Research Conference (MERCon), Moratuwa, Sri Lanka, 2019, pp. 538-543, doi: 10.1109/MERCon.2019.8818865.
[11]. F. Pacheco, E. Exposito, J. Aguilar, M. Gineste and C. Baudoin, "A novel statistical based feature extraction approach for the inner-class feature estimation using linear regression," 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil, 2018, pp. 1-8, doi: 10.1109/IJCNN.2018.8488992.
[12]. Teske, R. Falcon, R. Abielmona and E. Petriu, "Automatic Identification of Maritime Incidents from Unstructured Articles," 2018 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA), Boston, MA, USA, 2018, pp. 42-48, doi: 10.1109/COGSIMA.2018.8423975.
[13]. Aldallal and A. A. A. Al-Moosa, "Using Data Mining Techniques to Predict Diabetes and Heart Diseases," 2018 4th International Conference on Frontiers of Signal Processing (ICFSP), Poitiers, France, 2018, pp. 150-154, doi: 10.1109/ICFSP.2018.8552051.
[14]. X. Zhai et al., "Research on the Biological Basis of Treating Different Diseases with Same Method Based on Big Data Mining and Complex Network," 2018 IEEE 4th International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing, (HPSC) and IEEE International Conference on Intelligent Data and Security (IDS), Omaha, NE, USA, 2018, pp. 264-269, doi: 10.1109/BDS/HPSC/IDS18.2018.00063.
[15]. S. K. K. L, N. K. G and M. J. A, "Coronary Artery Disease Prediction using Data Mining Techniques," 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), Thoothukudi, India, 2020, pp. 693-697, doi: 10.1109/ICISS49785.2020.9316014.