Diagnosis of Alzheimer’s Disease using Comparative Study on Machine Learning Models

Ghulam Zohra, Muhammad Sohaib Akram, Saif ud Din


The method of diagnosing and treating diseases can be improved by identifying the genes that cause diseases. Alzheimer’s disease (AD) is one of the neurodegenerative disease that slowly destroys memory as well as thinking abilities. It’s important to diagnose Alzheimer’s disease (AD) early on so that adequate treatment can be given to patient. That article compares various machine learning models for identify Alzheimer’s Disease and proves that which algorithm gives the most reliable results in detecting AD in advance. Machine learning is a backbone of technology and everything in our life related to machine learning technologies. In this study various biomarkers are developed based on different machine learning classifiers like Random Forest, K-NN, Support Vector Machine, AdaBoost and XgBoost for AD gene detection. Genome data is extracted from NCBI related to Alzheimer disease. After that features are extracted from this genome data. Then above machine learning classifiers are train on these features. Different results are obtained by using Self-Consistency test and 10 Cross Validation test. Random Forest in both test gives 100% results. KNN gives 73.17% and 86.33%, SVM gives 100% and 97% AdaBoost gives 74.02% and 87.42%, XgBoost gives 86.04%and 92.56%accuracy for self-consistency and 10 Cross Validation test respectively.

Full Text:



X. Hong et al., “Predicting Alzheimer’s Disease Using LSTM,” IEEE Access, vol. 7, pp. 80893–80901, 2019.

R. Sivakani and G. A. Ansari, “Machine Learning Framework for Implementing Alzheimer’s Disease,” Proc. 2020 IEEE Int. Conf. Commun. Signal Process. ICCSP 2020, pp. 588–592, 2020.

S. Z. Paylakhi, S. Ozgoli, and S. H. Paylakhi, “A novel gene selection method using GA/SVM and Fisher criteria in Alzheimer’s disease,” ICEE 2015 - Proc. 23rd Iran. Conf. Electr. Eng., vol. 10, pp. 956–959, 2015.

M. Donini, M. Monteiro, and M. Pontil, “A MULTIMODAL MULTIPLE KERNEL LEARNING APPROACH TO ALZHEIMER ’ S DISEASE DETECTION for the Alzheimer ’ s Disease Neuroimaging Initiative ∗ 1 - Max Planck University College London Centre for Computational Psychiatry and Ageing Research , University College,” 2016.

E. Jabason, M. O. Ahmad, and M. N. S. Swamy, “Missing Structural and Clinical Features Imputation for Semi-supervised Alzheimer’s Disease Classification using Stacked Sparse Autoencoder,” 2018 IEEE Biomed. Circuits Syst. Conf. BioCAS 2018 - Proc., pp. 1–4, 2018.

L. Yue et al., “Auto-detection of alzheimer’s disease using deep convolutional neural networks,” ICNC-FSKD 2018 - 14th Int. Conf. Nat. Comput. Fuzzy Syst. Knowl. Discov., pp. 228–234, 2018.

L. Xu, G. Liang, C. Liao, G. Den Chen, and C. C. Chang, “An efficient classifier for Alzheimer’s disease genes identification,” Molecules, vol. 23, no. 12, 2018.

G. Uysal and M. Ozturk, “Using machine learning methods for detecting alzheimer’s disease through hippocampal volume analysis,” TIPTEKNO 2019 - Tip Teknol. Kongresi, no. 2018, pp. 1–4, 2019.

P. Lodha, A. Talele, and K. Degaonkar, “Diagnosis of Alzheimer’s Disease Using Machine Learning,” Proc. - 2018 4th Int. Conf. Comput. Commun. Control Autom. ICCUBEA 2018, pp. 1–4, 2018.

E. Jabason, M. O. Ahmad, and M. N. S. Swamy, “Classification of Alzheimer’s Disease from MRI Data Using an Ensemble of Hybrid Deep Convolutional Neural Networks,” Midwest Symp. Circuits Syst., vol. 2019-Augus, no. Mci, pp. 481–484, 2019.

G. He, A. Ping, X. Wang, and Y. Zhu, “Alzheimer’s disease diagnosis model based on three-dimensional full convolutional densenet,” Proc. - 10th Int. Conf. Inf. Technol. Med. Educ. ITME 2019, pp. 13–17, 2019.

P. K. Kotturu and A. Kumar, “Comparative study on machine learning models for early diagnose of Alzheimer’s Disease: Multi correlation method,” Proc. 5th Int. Conf. Commun. Electron. Syst. ICCES 2020, no. Icces, pp. 778–783, 2020.

M. Amin-Naji, H. Mahdavinataj, and A. Aghagolzadeh, “Alzheimer’s disease diagnosis from structural MRI using Siamese convolutional neural network,” 4th Int. Conf. Pattern Recognit. Image Anal. IPRIA 2019, pp. 75–79, 2019.

D. B. Akhila, S. Shobhana, A. L. Fred, and S. N. Kumar, “Robust Alzheimer’s disease classification based on multimodal neuroimaging,” Proc. 2nd IEEE Int. Conf. Eng. Technol. ICETECH 2016, no. March, pp. 748–752, 2016.

B. S. Mahanand, G. S. Babu, S. Suresh, and N. Sundararajan, “Identification of imaging biomarkers responsible for Alzheimer’s Disease using a McRBFN classifier,” Proc. - 2015 Int. Conf. Cogn. Comput. Inf. Process. CCIP 2015, 2015.

D. Manzak, G. Cetinel, and A. Manzak, “Automated Classification of Alzheimer’s Disease using Deep Neural Network (DNN) by Random Forest Feature Elimination,” 14th Int. Conf. Comput. Sci. Educ. ICCSE 2019, no. Iccse, pp. 1050–1053, 2019.

S. L. Mestizo Gutiérrez, M. Herrera Rivero, N. Cruz Ramírez, E. Hernández, and G. E. Aranda-Abreu, “Decision trees for the analysis of genes involved in Alzheimer[U+05F3]s disease pathology,” J. Theor. Biol., vol. 357, pp. 21–25, 2014.

G. Fiscon, E. Weitschek, M. C. De Cola, G. Felici, and P. Bertolazzi, “An integrated approach based on EEG signals processing combined with supervised methods to classify Alzheimer’s disease patients,” Proc. - 2018 IEEE Int. Conf. Bioinforma. Biomed. BIBM 2018, pp. 2750–2752, 2019.

M. Mostafa, A. El, and Y. M. K. Omar, “to Alzheimer ’ s Disease Using Support Vector Machine,” pp. 5–9.

F. F. Sherif, N. Zayed, and M. Fakhr, “Discovering Alzheimer Genetic Biomarkers Using Bayesian Networks,” Adv. Bioinformatics, vol. 2015, pp. 1–9, 2015.

C. S. Eke, E. Jammeh, X. Li, C. Carroll, S. Pearson, and E. Ifeachor, “Early Detection of Alzheimer’s Disease with Blood Plasma Proteins Using Support Vector Machines,” IEEE J. Biomed. Heal. Informatics, vol. 25, no. 1, pp. 218–226, 2021.

A. Aljović, A. Badnjević, and L. Gurbeta, “Artificial neural networks in the discrimination of Alzheimer’s disease using biomarkers data,” 2016 5th Mediterr. Conf. Embed. Comput. MECO 2016 - Incl. ECyPS 2016, BIOENG.MED 2016, MECO Student Chall. 2016, pp. 286–289, 2016.

S. Harish and K. S. Gayathri, “Smart home based prediction of symptoms of alzheimer’s disease using machine learning and contextual approach,” ICCIDS 2019 - 2nd Int. Conf. Comput. Intell. Data Sci. Proc., pp. 1–6, 2019.

A. Dey, “Machine Learning Algorithms: A Review,” Int. J. Comput. Sci. Inf. Technol., vol. 7, no. 3, pp. 1174–1179, 2016.

S. Mishra, R. K. Mallick, and D. A. Gadanayak, “Islanding Detection of Microgrid using EMD and Random Forest Classifier,” Int. Conf. Comput. Intell. Smart Power Syst. Sustain. Energy, CISPSSE 2020, vol. 2, no. 1, pp. 1–5, 2020.

M. Belgiu and L. Drăgu, “Random forest in remote sensing: A review of applications and future directions,” ISPRS J. Photogramm. Remote Sens., vol. 114, pp. 24–31, 2016.

M. Asadi and K. Pourhossein, “Locating Renewable Energy Generators Using K-Nearest Neighbors (KNN) Algorithm,” 2019 Iran. Conf. Renew. Energy Distrib. Gener. ICREDG 2019, pp. 11–12, 2019.

F. Wang, Z. Li, F. He, R. Wang, W. Yu, and F. Nie, “Feature Learning Viewpoint of Adaboost and a New Algorithm,” IEEE Access, vol. 7, pp. 149890–149899, 2019.

L. Torlay, M. Perrone-Bertolotti, E. Thomas, and M. Baciu, “Machine learning–XGBoost analysis of language networks to classify patients with epilepsy,” Brain Informatics, vol. 4, no. 3, pp. 159–169, 2017.

Y. Jiang, G. Tong, H. Yin, and N. Xiong, “A Pedestrian Detection Method Based on Genetic Algorithm for Optimize XGBoost Training Parameters,” IEEE Access, vol. 7, pp. 118310–118321, 2019.

Saeed, S.; Mahmood, M. K.; Khan, Y. D., An exposition of facial expression recognition techniques. Neural Computing and Applications 2018, 29 (9), 425-443.

Butt, A. H.; Khan, Y. D., CanLect-Pred: A cancer therapeutics tool for prediction of target cancerlectins using experiential annotated proteomic sequences. IEEE Access 2019, 8, 9520-9531.

Amanat, S.; Ashraf, A.; Hussain, W.; Rasool, N.; Khan, Y. D., Identification of lysine carboxylation sites in proteins by integrating statistical moments and position relative features via general PseAAC. Current Bioinformatics 2020, 15 (5), 396-407.

Ilyas, S., Hussain, W., Ashraf, A., Khan, Y. D., Khan, S. A., & Chou, K. C. (2019). iMethylK-PseAAC: Improving accuracy of lysine methylation sites identification by incorporating statistical moments and position relative features into general PseAAC via Chou’s 5-steps rule. Current Genomics, 20(4), 275-292.

Hussain, W.; Rasool, N.; Khan, Y. D., A Sequence-Based Predictor of Zika Virus Proteins Developed by Integration of PseAAC and Statistical Moments. Combinatorial chemistry & high throughput screening 2020, 23 (8), 797-804.

Khan, Y. D.; Alzahrani, E.; Alghamdi, W.; Ullah, M. Z., Sequence-based Identification of Allergen Proteins Developed by Integration of PseAAC and Statistical Moments via 5-Step Rule. Current Bioinformatics 2020, 15 (9), 1046-1055.

Mahmood, M. K.; Ehsan, A.; Khan, Y. D.; Chou, K.-C., iHyd-LysSite (EPSV): Identifying Hydroxylysine Sites in Protein Using Statistical Formulation by Extracting Enhanced Position and Sequence Variant Feature Technique. Current Genomics 2020, 21 (7), 536-545.

Naseer, S.; Hussain, W.; Khan, Y. D.; Rasool, N., IPhosS (Deep)-PseAAC: Identify phosphoserine sites in proteins using deep learning on general pseudo amino acid compositions via modified 5-Steps rule. IEEE/ACM Transactions on Computational Biology and Bioinformatics 2020.

Naseer, S.; Hussain, W.; Khan, Y. D.; Rasool, N., Sequence-based identification of arginine amidation sites in proteins using deep representations of proteins and PseAAC. Current Bioinformatics 2020, 15 (8), 937-948.

Shah, A. A.; Khan, Y. D., Identification of 4-carboxyglutamate residue sites based on position based statistical feature and multiple classification. Scientific Reports 2020, 10 (1), 1-10.

Awais, M.; Hussain, W.; Rasool, N.; Khan, Y. D., iTSP-PseAAC: Identifying Tumor Suppressor Proteins by Using Fully Connected Neural Network and PseAAC. Current Bioinformatics 2021, 16 (5), 700-709.

Hussain, W.; Rasool, N.; Khan, Y. D., Insights into Machine Learning-based approaches for Virtual Screening in Drug Discovery: Existing strategies and streamlining through FP-CADD. Current Drug Discovery Technologies 2021, 18 (4), 463-472.

Khan, Y. D.; Khan, N. S.; Naseer, S.; Butt, A. H., iSUMOK-PseAAC: prediction of lysine sumoylation sites using statistical moments and Chou’s PseAAC. PeerJ 2021, 9, e11581.

Malebary, S. J.; Khan, R.; Khan, Y. D., ProtoPred: Advancing Oncological Research Through Identification of Proto-Oncogene Proteins. IEEE Access 2021, 9, 68788-68797.

Malebary, S. J.; Khan, Y. D., Evaluating machine learning methodologies for identification of cancer driver genes. Scientific reports 2021, 11 (1), 1-13.

Malebary, S. J.; Khan, Y. D., Identification of Antimicrobial Peptides Using Chou's 5 Step Rule. CMC-COMPUTERS MATERIALS & CONTINUA 2021, 67 (3), 2863-2881.

Naseer, S.; Ali, R. F.; Khan, Y. D.; Dominic, P., iGluK-Deep: computational identification of lysine glutarylation sites using deep neural networks with general pseudo amino acid compositions. Journal of Biomolecular Structure and Dynamics 2021, 1-14.

Naseer, S.; Hussain, W.; Khan, Y. D.; Rasool, N., NPalmitoylDeep-PseAAC: A Predictor of N-Palmitoylation Sites in Proteins Using Deep Representations of Proteins and PseAAC via Modified 5-Steps Rule. Current Bioinformatics 2021, 16 (2), 294-305.

Naseer, S.; Hussain, W.; Khan, Y. D.; Rasool, N., Optimization of serine phosphorylation prediction in proteins by comparing human engineered features and deep representations. Analytical Biochemistry 2021, 615, 114069.

Khanum, S., Ashraf, M. A., Karim, A., Shoaib, B., Khan, M. A., Naqvi, R. A., ... & Alswaitti, M. Gly-LysPred: Identification of Lysine Glycation Sites in Protein Using Position Relative Features and Statistical Moments via Chou’s 5 Step Rule.

Lv, H., Dao, F. Y., Zhang, D., Yang, H., & Lin, H. (2021). Advances in mapping the epigenetic modifications of 5‐methylcytosine (5mC), N6‐methyladenine (6mA), and N4‐methylcytosine (4mC). Biotechnology and Bioengineering.

Zulfiqar, H., Sun, Z. J., Huang, Q. L., Yuan, S. S., Lv, H., Dao, F. Y., ... & Li, Y. W. (2021). Deep-4mCW2V: A sequence-based predictor to identify N4-methylcytosine sites in Escherichia coli. Methods.

Liu, Y., Wang, X., & Liu, B. (2019). A comprehensive review and comparison of existing computational methods for intrinsically disordered protein and region prediction. Briefings in bioinformatics, 20(1), 330-346.

Zhang, D., Xu, Z. C., Su, W., Yang, Y. H., Lv, H., Yang, H., & Lin, H. (2021). iCarPS: a computational tool for identifying protein carbonylation sites by novel encoded features. Bioinformatics, 37(2), 171-177

DOI: http://dx.doi.org/10.21015/vtse.v9i1.750


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.