Predicting the transition from Mild Cognitive Impairment to Alzheimer’s disease using Cognitive tests and MRI measures of Demographic Data with an Ensemble Model


  • Pothala Ramya Research Scholar, Department of Computer Science and Engineering, JNTUK University, Kakinada, Andhra Pradesh
  • Ch. Ramesh Professor(CSE) & Dean (IQAC), Aditya Institute of Technology and Management, Tekkali Srikakulam(Dt), Andhra Pradesh, India.
  • O. Srinivasa Rao Professor, Head of Computer Science and Engineering Department, JNTUK University, Kakinada, Andhra Pradesh, India


Machine Learning, Ensemble, ADNIMERGE, Feature selection, principal component analysis, Alzheimer's disease (AD)


Dementia, one of the most dreaded illnesses, has an enormous annual impact on health and social care expenses worldwide than cancer and chronic heart disease. Despite the lack of a treatment or standardized clinical test, using machine learning techniques to identify people at risk of developing Dementia could represent a new step toward proactive management. Cerebrospinal fluid (CSF), positron emission tomography (PET) scans, magnetic resonance imaging (MRI), biological markers (biomarkers), clinical scans, and neuropsychological therapy are all integrated to track the development of early Alzheimer's disease (AD) and moderate cognitive impairment (MCI). Early detection of Alzheimer's disease (AD) is crucial for controlling the illness, assistance, and the accessibility of healthcare resources. This study focused on the detection rate and false positive rate of a disease determined from ADNI-ADNIMERGE demographic data using a variety of machine learning techniques, including KNN, SVM, RF, NB, LOGISTIC, and Ensembled: LOGISTIC-PCA, SVM, KNN as the final algorithm with feature selection and hyper-tuning parameter optimization. Performed a comparison analysis between machine learning methods and ensemble model. Ensemble model showed best results with change in biomarker and baseline biomarker of disease detection rate, false positive rate and test accuracy 92%, 90% of AD respectively.


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Fadi Thabtah, Swan Ong and David Peebles (2022). Detection of Dementia Progression from Functional Activities Data using Machine Learning Techniques.

Erik D. Huckvale, Matthew W. Hodgman, Brianna B. Greenwood (2021). Pairwise Correlation Analysis of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) Dataset Reveals Significant Feature Correlation. Genes (Basel). 2021 Nov; 12(11): 1661. Published online 2021 Oct 21. doi: 10.3390/genes12111661, PMCID: PMC8619902, PMID: 34828267

Massimiliano Grassi, Nadine Rouleaux, Daniela Caldirola (2019). A Novel Ensemble-Based Machine Learning Algorithm to Predict the Conversion From Mild Cognitive Impairment to Alzheimer's Disease Using Socio-Demographic Characteristics, Clinical Information, and Neuropsychological Measures. Front Neurol. 2019; 10: 756. Published online 2019 Jul 16. doi: 10.3389/fneur.2019.00756. PMCID: PMC6646724. PMID: 31379711.

Jayant Prakash, Velda Wang, Robert E. Quinn (2021). Unsupervised Machine Learning to Identify Separable Clinical Alzheimer’s Disease Sub-Populations. Journals Brain Sciences Volume 11 Issue 8 10.3390/brainsci11080977.

Muhammad Irfan, Seyed Shahrestani and Mahmoud Elkhodr (2023). Enhancing Early Dementia Detection: A Machine Learning Approach Leveraging Cognitive and Neuroimaging Features for Optimal Predictive Performance. Appl. Sci. 2023, 13, 10470.

Afreen Khan, Swaleha Zubair (2022). Development of a three tiered cognitive hybrid machine learning algorithm for effective diagnosis of Alzheimer’s disease. Journal of King Saud University - Computer and Information Sciences, Volume 34, Issue 10, Part A, November 2022, Pages 8000-8018.

M. Tanveer, B. Richhariya and R. U. Khan (2020). Machine learning techniques for the diagnosis of Alzheimer’s disease. ACM Transactions on Multimedia Computing, Communications, and Applications, 16(1s), 1–35.

Esther E. Bron, Stefan Klein, Janne M. Papma (2021). Cross-cohort generalizability of deep and conventional machine learning for MRI-based diagnosis and prediction of Alzheimer’s disease. NeuroImage: Clinical, 31, 102712.

Jun Pyo Kima, Jeonghun Kimb, Yu Hyun Parka, Seong Beom Parka (2019). Machine learning based hierarchical classification of frontotemporal dementia and Alzheimer's disease. NeuroImage: Clinical, 23, 101811.

Mingxia Liuy, Jun Zhangy, Dong Nie, Pew-Thian Yap, Dinggang Shen, Fellow (2018). Anatomical landmark based deep feature representation for MR images in brain disease diagnosis. IEEE Journal of Biomedical and Health Informatics, 22(5), 1476–1485.

Binny Naik, Ashir Mehta and Manan Shah (2020). Denouements of Machine Learning and Multimodal Diagnostic Classification of Alzheimer’s disease. Visual Computing for Industry, Biomedicine, and Art, 3(1)

Samaneh Abolpour Mofrad, Arvid Lundervold, Alexander Selvikvåg Lundervold, (2021). A predictive framework based on brain volume trajectories enabling early detection of Alzheimer's disease. Computerized Medical Imaging and Graphics, 90, 101910.

F.J. Martinez-Murcia, A. Ortiz, J.M. Gorriz, J. Ramirez and D. Castillo-Barnes (2020). Studying the manifold structure of Alzheimer's disease: A deep learning approach using convolutional autoencoders. IEEE Journal of Biomedical and Health Informatics, 24(1), 17–26.

Suhad Al-Shoukry, Taha H. Rassem and Nasrin M. Makbol (2020). Alzheimer’s diseases detection by using Deep Learning Algorithms: A mini-review. IEEE Access, 8, 77131–77141.

Behnaz Ghoraani, Lillian N. Boettcher, Murtadha D. Hssayeni (2021). Detection of mild cognitive impairment and Alzheimer’s disease using dual-task gait assessments and machine learning. Biomedical Signal Processing and Control, 64, 102249.

Xia-an Bi, Xi Hu, Hao Wu, and Yang Wang (2020). Multimodal Data Analysis of Alzheimer's disease based on Clustering Evolutionary Random Forest. IEEE Journal of Biomedical and Health Informatics, 24(10), 2973–2983.

Ruhul Amin Hazarika, Ajith Abraham, Samarendra Nath Sur, Arnab Kumar Maji & Debdatta Kandar (2021). Different techniques for Alzheimer’s disease classification using brain images: A study. International Journal of Multimedia Information Retrieval, 10(4), 199–218.

K.R. Kruthika, Rajeswari, H.D. Maheshappa (2019). Multistage classifier-based approach for Alzheimer's disease prediction and retrieval. Informatics in Medicine Unlocked, 14, 34–42.

Jyoti Islam and Yanqing Zhang (2018). Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional Neural Networks. Brain Informatics, 5(2).

Naimul Mefraz Khan, Nabila Abraham, Marcia Hon (2019). Transfer learning with intelligent training data selection for prediction of Alzheimer’s disease. IEEE Access, 7, 72726–72735.

Zhao Fan, Fanyu Xu, Xuedan Qi, Cai Li, Lili Yao (2019). Classification of Alzheimer’s disease based on Brain MRI and machine learning. Neural Computing and Applications, 32(7), 1927–1936.

P. Kishore, Ch. Usha Kumari, M.N.V.S.S. Kumar, T. Pavani (2021). Detection and analysis of alzheimer’s disease using various machine learning algorithms. Materials Today: Proceedings, 45, 1502–1508.

Jack Albright. (2019). Forecasting the progression of alzheimer's disease using neural networks and a novel preprocessing algorithm. Alzheimer's & Dementia: Translational Research & Clinical Interventions, 5(1), 483–491.

Manan Binth Taj Noor, Nusrat Zerin Zenia, M Shamim Kaiser, Shamim A Mamun and Mufti Mahmud (2020). Application of deep learning in detecting neurological disorders from Magnetic Resonance Images: A survey on the detection of alzheimer’s disease, parkinson’s disease and schizophrenia. Brain Informatics, 7(1).

Yousry AbdulAzeem, Waleed M. Bahgat, Mahmoud Badawy (2021). A CNN based framework for classification of alzheimer’s disease. Neural Computing and Applications, 33(16), 10415–10428.

U. Rajendra Acharya, Steven Lawrence Fernandes, Joel En WeiKoh & Edward J. Ciaccio (2019). Automated detection of alzheimer’s disease using brain MRI images– a study with various feature extraction techniques. Journal of Medical Systems, 43(9).

Kueper, J. K., Speechley, M., & Montero-Odasso, M. (2018). The Alzheimer’s Disease Assessment Scale-Cognitive Subscale (ADAS-Cog): Modifications and responsiveness in pre-dementia populations. A narrative review. Journal of Alzheimer's Disease, 63(2), 423–444.

Park, S.-H., & Han, K. S. (2022). Is the Alzheimer's Disease Assessment Scale-Cognitive Subscale useful in screening for mild cognitive impairment and Alzheimer's disease? A systematic review. Current Alzheimer Research, 19(3), 202–211.




How to Cite

Ramya, P. ., Ramesh, C. ., & Rao, O. S. . (2023). Predicting the transition from Mild Cognitive Impairment to Alzheimer’s disease using Cognitive tests and MRI measures of Demographic Data with an Ensemble Model. International Journal of Intelligent Systems and Applications in Engineering, 12(2), 250–268. Retrieved from



Research Article