{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:38:53Z","timestamp":1787027933255,"version":"build-2736575974"},"reference-count":47,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2024,12,10]],"date-time":"2024-12-10T00:00:00Z","timestamp":1733788800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Alzheimer\u2019s disease (AD) is a progressive neurodegenerative disorder and is the most common cause of dementia. Early diagnosis of Alzheimer\u2019s disease is critical for better management and treatment outcomes, but it remains a challenging task due to the complex nature of the disease. Clinical data, including a range of cognitive, functional, and demographic variables, play a crucial role in Alzheimer\u2019s disease classification. Also, challenges such as data imbalance and high-dimensional feature sets often hinder model performance. Objective: This paper aims to propose a computationally efficient, reliable, and transparent machine learning-based framework for the classification of Alzheimer\u2019s disease patients. This framework is interpretable and helps medical practitioners learn complex patterns in patients. Method: This study addresses these issues by employing boosting algorithms, for enhanced classification accuracy. To mitigate data imbalance, a random sampling technique is applied, ensuring a balanced representation of Alzheimer\u2019s and healthy cases. Extensive feature analysis was conducted to identify the most impactful clinical features followed by feature reduction techniques to focus on the most informative clinical features, reducing model complexity and overfitting risks. Explainable AI tools, such as SHAP, LIME, ALE, and ELI5 are integrated to provide transparency into the model\u2019s decision-making process, highlighting key features influencing the classification and allowing clinicians to understand and trust the key features driving the predictions. Results: This approach results in a robust, interpretable, and clinically relevant framework for Alzheimer\u2019s disease diagnosis. The proposed approach achieved the best accuracy of 95%, demonstrating its effectiveness and potential for reliable early diagnosis of Alzheimer\u2019s disease. Conclusions: This study demonstrates that integrating ensemble learning algorithms and explainable AI, while using a balanced dataset with feature selection, improves quantitative results and interpretability. This approach offers a promising method for early and better-informed clinical decisions.<\/jats:p>","DOI":"10.3390\/diagnostics14242770","type":"journal-article","created":{"date-parts":[[2024,12,10]],"date-time":"2024-12-10T04:15:03Z","timestamp":1733804103000},"page":"2770","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Efficient Explainable Models for Alzheimer\u2019s Disease Classification with Feature Selection and Data Balancing Approach Using Ensemble Learning"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8113-3275","authenticated-orcid":false,"given":"Yogita","family":"Dubey","sequence":"first","affiliation":[{"name":"Department of Electronics and Telecommunication, Yeshwantrao Chavan College of Engineering, Nagpur 441110, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aditya","family":"Bhongade","sequence":"additional","affiliation":[{"name":"Department of Electronics and Telecommunication, Yeshwantrao Chavan College of Engineering, Nagpur 441110, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6036-2842","authenticated-orcid":false,"given":"Prachi","family":"Palsodkar","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, Yeshwantrao Chavan College of Engineering, Nagpur 441110, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1048-3144","authenticated-orcid":false,"given":"Punit","family":"Fulzele","sequence":"additional","affiliation":[{"name":"Directorate of Research and Innovation, SPDC, Datta Meghe Institute of Higher Education and Research, Wardha 442001, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Huang, L.K., Kuan, Y.C., Lin, H.W., and Hu, C.J. (2023). Clinical trials of new drugs for Alzheimer disease: A 2020\u20132023 update. J. Biomed. Sci., 30.","DOI":"10.1186\/s12929-023-00976-6"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1186\/s40708-023-00195-7","article-title":"A systematic review on machine learning and deep learning techniques in the effective diagnosis of Alzheimer\u2019s disease","volume":"10","author":"Arya","year":"2023","journal-title":"Brain Inform."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Alshamlan, H., Omar, S., Aljurayyad, R., and Alabduljabbar, R. (2023). Identifying effective feature selection methods for Alzheimer\u2019s disease biomarker gene detection using machine learning. Diagnostics, 13.","DOI":"10.3390\/diagnostics13101771"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Alamro, H., Thafar, M.A., Albaradei, S., Gojobori, T., Essack, M., and Gao, X. (2023). Exploiting machine learning models to identify novel Alzheimer\u2019s disease biomarkers and potential targets. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-30904-5"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1186\/s13195-023-01195-9","article-title":"Assessment of Alzheimer-related pathologies of dementia using machine learning feature selection","volume":"15","author":"Rajab","year":"2023","journal-title":"Alzheimer\u2019s Res. Ther."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.matpr.2021.03.061","article-title":"Alzheimer\u2019s disease prediction using machine learning techniques and principal component analysis (PCA)","volume":"81","author":"Sudharsan","year":"2023","journal-title":"Mater. Today Proc."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Lmatrafi, S., and Abbas QIbrahim, M.E.A. (2024). A systematic literature review of machine learning approaches for class-wise recognition of Alzheimer\u2019s disease using neuroimaging-based brain disorder analysis. Multimed Tools Appl.","DOI":"10.1007\/s11042-024-19104-z"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1186\/s41983-022-00571-w","article-title":"Early prediction of Alzheimer\u2019s disease using convolutional neural network: A review","volume":"58","author":"Patil","year":"2022","journal-title":"Egypt. J. Neurol. Psychiatry Neurosurg."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_11","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T. (2017, January 4\u20139). LightGBM: A highly efficient gradient boosting decision tree. Proceedings of the 31st International Conference on Neural Information Processing Systems (NeurIPS), Long Beach, CA, USA."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"349","DOI":"10.4310\/SII.2009.v2.n3.a8","article-title":"Multi-class AdaBoost","volume":"2","author":"Hastie","year":"2009","journal-title":"Stat. Its Interface"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jcss.1997.1504","article-title":"A decision-theoretic generalization of on-line learning and an application to boosting","volume":"55","author":"Freund","year":"1997","journal-title":"J. Comput. Syst. Sci."},{"key":"ref_14","first-page":"549","article-title":"MultiBoost: A multi-purpose boosting package","volume":"13","author":"Benbouzid","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_15","unstructured":"Luo, P., Xiong, Y., Liu, W., and Tang, X. (2016, January 13\u201317). Efficient mini-batch training for stochastic gradient boosting decision trees. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_17","first-page":"18","article-title":"Classification and regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"102928","DOI":"10.1016\/j.artmed.2024.102928","article-title":"A systematic literature review on the significance of deep learning and machine learning in predicting Alzheimer\u2019s disease","volume":"154","author":"Kaur","year":"2024","journal-title":"Artif. Intell. Med."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Alatrany, A.S., Khan, W., Hussain, A., Kolivand, H., and Al-Jumeily, D. (2024). An explainable machine learning approach for Alzheimer\u2019s disease classification. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-51985-w"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3236009","article-title":"A survey of methods for explaining black box models","volume":"51","author":"Guidotti","year":"2018","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_21","unstructured":"Molnar, C. (2024, September 01). Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. Available online: https:\/\/christophm.github.io\/interpretable-ml-book\/."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4793","DOI":"10.1109\/TNNLS.2020.3027314","article-title":"A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI","volume":"32","author":"Tjoa","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Linardatos, P., Papastefanopoulos, V., and Kotsiantis, S. (2021). Explainable AI: A Review of Machine Learning Interpretability Methods. Entropy, 23.","DOI":"10.3390\/e23010018"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Yi, F., Yang, H., Chen, D., Qin, Y., Han, H., Cui, J., Bai, W., Ma, Y., Zhang, R., and Yu, H. (2023). XGBoost-SHAP-based interpretable diagnostic framework for alzheimer\u2019s disease. BMC Med. Inform. Decis. Mak., 23.","DOI":"10.1186\/s12911-023-02238-9"},{"key":"ref_25","first-page":"4306","article-title":"An interpretable machine learning-based breast cancer classification using XGBoost, SHAP, and LIME","volume":"13","author":"Dutta","year":"2024","journal-title":"Bull. Electr. Eng. Inform."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Thakur, A., Arunbalaji, C.G., Maddi, A., and Maheswari, B.U. (2024, January 8\u20139). Interpretable Predictive Modeling for Smoking and Drinking Behavior using SHAP and LIME. Proceedings of the 2024 International Conference on Current Trends in Advanced Computing (ICCTAC), Bengaluru, India.","DOI":"10.1109\/ICCTAC61556.2024.10581153"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2957","DOI":"10.1007\/s11069-022-05793-y","article-title":"Predicting and analyzing flood susceptibility using boosting-based ensemble machine learning algorithms with SHapley Additive exPlanations","volume":"116","author":"Aydin","year":"2023","journal-title":"Nat. Hazards"},{"key":"ref_28","first-page":"e01059","article-title":"A novel approach to explain the black-box nature of machine learning in compressive strength predictions of concrete using Shapley additive explanations (SHAP)","volume":"16","author":"Ekanayake","year":"2022","journal-title":"Case Stud. Constr. Mater."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5447","DOI":"10.1007\/s10639-023-12007-w","article-title":"An empirical assessment of smote variants techniques and interpretation methods in improving the accuracy and the interpretability of student performance models","volume":"29","author":"Sahlaoui","year":"2024","journal-title":"Educ. Inf. Technol."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ma, X., Hou, M., Zhan, J., and Liu, Z. (2023). Interpretable predictive modeling of tight gas well productivity with SHAP and LIME techniques. Energies, 16.","DOI":"10.3390\/en16093653"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"116890","DOI":"10.1016\/j.jenvman.2022.116890","article-title":"An interpretable machine learning approach based on DNN, SVR, Extra Tree, and XGBoost models for predicting daily pan evaporation","volume":"327","author":"Abdeslam","year":"2023","journal-title":"J. Environ. Manag."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1186\/s40708-024-00222-1","article-title":"Interpreting artificial intelligence models: A systematic review on the application of LIME and SHAP in Alzheimer\u2019s disease detection","volume":"11","author":"Vimbi","year":"2024","journal-title":"Brain Inform."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., and Guestrin, C. (2016, January 13\u201317). \u201cWhy should I trust you?\u201d: Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939778"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., and Guestrin, C. (2018, January 2\u20137). Anchors: High-precision model-agnostic explanations. Proceedings of the AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.11491"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","article-title":"Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI","volume":"58","author":"Arrieta","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_36","unstructured":"Islam, M., Zhang, Y., Venkatesh, S., Mahmud, M., and Venkatesh, S. (2020, January 19\u201324). Interpretable machine learning models for Alzheimer\u2019s disease classification with LIME feature importance analysis. Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK."},{"key":"ref_37","unstructured":"Lundberg, S. (2017). A unified approach to interpreting model predictions. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","article-title":"From local explanations to global understanding with explainable AI for trees","volume":"2","author":"Lundberg","year":"2020","journal-title":"Nat. Mach. Intell."},{"key":"ref_39","unstructured":"Liu, F.X. (2024). Evaluating the Impact of Data Augmentation on Explainable AI in Medical Image Analysis. [Master\u2019s Thesis, Eindhoven University of Technology]. Available online: https:\/\/research.tue.nl\/en\/studentTheses\/evaluating-the-impact-of-data-augmentation-on-explainable-ai-in-m."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"114214","DOI":"10.1016\/j.dss.2024.114214","article-title":"Towards explainable artificial intelligence through expert-augmented supervised feature selection","volume":"181","author":"Rabiee","year":"2024","journal-title":"Decis. Support Syst."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1111\/rssb.12377","article-title":"Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models","volume":"82","author":"Apley","year":"2020","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol."},{"key":"ref_42","first-page":"171","article-title":"Conversation analysis: An ELI5 overview","volume":"15","author":"Ghahraman","year":"2021","journal-title":"Int. J. Lang. Stud."},{"key":"ref_43","unstructured":"(2024, September 01). Available online: https:\/\/www.kaggle.com\/datasets\/rabieelkharoua\/alzheimers-disease-dataset."},{"key":"ref_44","first-page":"9917919","article-title":"A comparative analysis of machine learning algorithms to predict Alzheimer\u2019s disease","volume":"2021","author":"Antor","year":"2021","journal-title":"J. Healthc. Eng."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Kavitha, C., Mani, V., Srividhya, S.R., Khalaf, O.I., and Romero, C.A.T. (2022). Early-stage Alzheimer\u2019s disease prediction using machine learning models. Front. Public Health, 10.","DOI":"10.3389\/fpubh.2022.853294"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"95091","DOI":"10.1109\/ACCESS.2024.3410135","article-title":"Explainable Machine Learning Model for Alzheimer Detection Using Genetic Data: A Genome-Wide Association Study Approach","volume":"12","author":"Khater","year":"2024","journal-title":"IEEE Access"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"123173","DOI":"10.1109\/ACCESS.2023.3328331","article-title":"Explainable Artificial Intelligence of Multi-Level Stacking Ensemble for Detection of Alzheimer\u2019s Disease Based on Particle Swarm Optimization and the Sub-Scores of Cognitive Biomarkers","volume":"11","author":"AlMohimeed","year":"2023","journal-title":"IEEE Access"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/14\/24\/2770\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:50:55Z","timestamp":1760115055000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/14\/24\/2770"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,10]]},"references-count":47,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["diagnostics14242770"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics14242770","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,10]]}}}