Early Detection of Alzheimer's Disease Using Fuzzy C-Means Clustering and Genetic Algorithm-Based Feature Selection from PET Scans
Keywords:
Early Detection Disease, Fuzzy C Means Clustering, Genetic Algorithm, feature SelectionAbstract
Early identification is essential for successful intervention in Alzheimer's disease (AD), a crippling neurological ailment that affects millions of people worldwide. Using fuzzy C-Means (FCM) clustering and feature selection based on genetic algorithms (GA), this work proposes a novel method for the early identification of AD using positron emission tomography (PET) data.The suggested approach begins by extracting a broad range of data from PET scans, which cover multiple spatial and intensity-based properties of brain regions. These characteristics may be used as AD discriminative indicators. A GA is used to perform feature selection, selecting the most informative subset of features, in order to improve the discriminative power of the feature set and decrease redundancy.FCM clustering is then utilised on the chosen feature subset. A more detailed characterisation of brain regions is possible because to the soft clustering method FCM, which gives membership degrees to each data point. We want to uncover different glucose metabolism patterns that can distinguish AD patients from healthy people using FCM.We achieve two main goals by combining FCM clustering and GA-based feature selection. The classification process becomes more effective and understandable in the first place by reducing the dimensionality of the feature space. Second, it improves the distinction between AD and non-AD clusters, increasing the precision of early AD identification.On a dataset made up of PET scans from both AD patients and healthy controls, extensive experiments are performed to assess the suggested technique. The data show the efficacy of our strategy in precisely identifying those at risk of AD at an early stage, hence allowing for prompt therapies and better patient outcomes.
Downloads
References
A. Balakrishnan, J. Medikonda, P. K. Namboothiri and M. Natarajan, "Parkinson’s Disease Stage Classification with Gait Analysis using Machine Learning Techniques and SMOTE-based Approach for Class Imbalance Problem," 2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics ( DISCOVER), Shivamogga, India, 2022, pp. 277-281, doi: 10.1109/DISCOVER55800.2022.9974754.
C. H. Reddy and M. Kanchana, "Artificial Intelligence towards Parkinson’s disease Diagnosis: A systematic Review of Contemporary Literature," 2022 IEEE 4th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), Goa, India, 2022, pp. 31-37, doi: 10.1109/ICCCMLA56841.2022.9988755.
H. Lei et al., "Parkinson's Disease Diagnosis via Joint Learning From Multiple Modalities and Relations," in IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 4, pp. 1437-1449, July 2019, doi: 10.1109/JBHI.2018.2868420.
M. Nithya, V. Lalitha, K. Paveethra and S. Kumari, "Early Detection of Parkinson's Disease using Machine Learning & Image Processing," 2022 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India, 2022, pp. 1-4, doi: 10.1109/ICCCI54379.2022.9740961.
Z. Huang, H. Lei, S. Li, X. Xiao, E. -L. Tan and B. Lei, "Longitudinal Feature Selection and Feature Learning for Parkinson's Disease Diagnosis and Prediction," 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy, 2021, pp. 5736-5743, doi: 10.1109/ICPR48806.2021.9413040.
M. Saxena and S. Ahuja, "Comparative Survey of Machine Learning Techniques for Prediction of Parkinson's Disease," 2020 Indo – Taiwan 2nd International Conference on Computing, Analytics and Networks (Indo-Taiwan ICAN), Rajpura, India, 2020, pp. 248-253, doi: 10.1109/Indo-TaiwanICAN48429.2020.9181368.
Y. Zhang, H. Lei, Z. Huang, Z. Li, C. -M. Liu and B. Lei, "Parkinson's Disease Classification with Self-supervised Learning and Attention Mechanism," 2022 26th International Conference on Pattern Recognition (ICPR), Montreal, QC, Canada, 2022, pp. 4601-4607, doi: 10.1109/ICPR56361.2022.9956213.
T. D. Pham and H. Yan, "Tensor Decomposition of Gait Dynamics in Parkinson's Disease," in IEEE Transactions on Biomedical Engineering, vol. 65, no. 8, pp. 1820-1827, Aug. 2018, doi: 10.1109/TBME.2017.2779884.
J. Faouzi et al., "Machine Learning-Based Prediction of Impulse Control Disorders in Parkinson’s Disease From Clinical and Genetic Data," in IEEE Open Journal of Engineering in Medicine and Biology, vol. 3, pp. 96-107, 2022, doi: 10.1109/OJEMB.2022.3178295.
S. Marar, D. Swain, V. Hiwarkar, N. Motwani and A. Awari, "Predicting the occurrence of Parkinson’s Disease using various Classification Models," 2018 International Conference on Advanced Computation and Telecommunication (ICACAT), Bhopal, India, 2018, pp. 1-5, doi: 10.1109/ICACAT.2018.8933579.
C. D. Anisha and N. Arulanand, "Early Prediction of Parkinson's Disease (PD) Using Ensemble Classifiers," 2020 International Conference on Innovative Trends in Information Technology (ICITIIT), Kottayam, India, 2020, pp. 1-6, doi: 10.1109/ICITIIT49094.2020.9071562.
M. M. Nishat, T. Hasan, S. M. Nasrullah, F. Faisal, M. A. -A. -R. Asif and M. A. Hoque, "Detection of Parkinson's Disease by Employing Boosting Algorithms," 2021 Joint 10th International Conference on Informatics, Electronics & Vision (ICIEV) and 2021 5th International Conference on Imaging, Vision & Pattern Recognition (icIVPR), Kitakyushu, Japan, 2021, pp. 1-7, doi: 10.1109/ICIEVicIVPR52578.2021.9564108.
Huiyi Wu et al., "A Deep Learning Method for Pathological Voice Detection using Convolutional Deep Belief Network", Research Gate, 2018.
Salama A. Mostafa et al., "Examining multiple feature evaluation and classification methods for improving the diagnosis of Parkinson's disease", Elseiver-Cognitive System Research., 2018.
ElmehdiBenmalek et al., "Voice Assessments for detecting Patients with Parkinson's diseases in different Stages", International Journal of Electrical and Computer Engineering (IJECE), 2018.
M. Bende, M. Khandelwal, D. Borgaonkar and P. Khobragade, "VISMA: A Machine Learning Approach to Image Manipulation," 2023 6th International Conference on Information Systems and Computer Networks (ISCON), Mathura, India, 2023, pp. 1-5, doi: 10.1109/ISCON57294.2023.10112168.
S. Ajani and M. Wanjari, "An Efficient Approach for Clustering Uncertain Data Mining Based on Hash Indexing and Voronoi Clustering," 2013 5th International Conference and Computational Intelligence and Communication Networks, 2013, pp. 486-490, doi: 10.1109/CICN.2013.106.
Khetani, V. ., Gandhi, Y. ., Bhattacharya, S. ., Ajani, S. N. ., & Limkar, S. . (2023). Cross-Domain Analysis of ML and DL: Evaluating their Impact in Diverse Domains. International Journal of Intelligent Systems and Applications in Engineering, 11(7s), 253–262.
Borkar, P., Wankhede, V.A., Mane, D.T. et al. Deep learning and image processing-based early detection of Alzheimer disease in cognitively normal individuals. Soft Comput (2023). https://doi.org/10.1007/s00500-023-08615-w
NawelSouissi et al., "Dimensionality Reduction for Voice Disorders Identification System Based on Mel Frequency Cepstral Coefficients and Support Vector Machine", 7th International Conference on Modeling Identification and Control (ICMIC 2015), 2015.
Ismail El Moudden et al., "Automatic Speech Analysis in Patients with Parkinson's Disease using Feature Dimension Reduction", Research gate - 3rd International Conference on Mechatronics and Robotics Engineering (ICMRE 2017), 2017.
AnujAnand et al., "Evaluation of Machine learning and Deep learning algorithms combined with dimentionality reduction techniques for classification of Parkinson's Disease", IEEE - International Symposium on Signal Processing and Information Technology, 2018.
Yunfeng Wu et al., "Dysphonic Voice Pattern Analysis of Patients in Parkinson's Disease Using Minimum Interclass Probability Risk Feature Selection and Bagging Ensemble Learning Methods", Machine Learning Approach to Dysphonia Detection, 2017.
Liaqat Ali et al., "Early diagnosis of Parkinson's disease from multiple voice recordings by simultaneous sample and feature selection", Elsevier - Expert system with Applications, 2019.
ZuzanaDankovicova et al., "Machine Learning Approaches to Dvsnhonia Detection", MDPI-Applied Science, 2018.
Ajani, S.N., Mulla, R.A., Limkar, S. et al. DLMBHCO: design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis. Soft Comput (2023). https://doi.org/10.1007/s00500-023-08613-y
NajmehFayyazifar et al., "Parkinson's disease detection using ensemble techniques and genetic algorithm", IEEE - Artificial Intelligence and Signal Processing Conference, 2017.
Akshaya Dinesh et al., "Using machine learning to diagnose Parkinson's disease from voice recordings", IEEE -MIT Undergraduate Research Technology Conference (URTC), 2017.
Minh Pham et al., "Diagnosing Voice Disorder with Machine Learning", IEEE International Conference on Big Data (Big Data), 2018.
PavolHarar et al., "Towards Robust Voice Pathology Detection Investigation of supervised deep learning gradient boosting and anomaly detection approaches across four databases", Neural Computing and Application, 2018.
Mark White, Thomas Wood, Carlos Rodríguez, Pekka Koskinen, Jónsson Ólafur. Machine Learning for Adaptive Assessment and Feedback. Kuwait Journal of Machine Learning, 2(1). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/169
Yanamadni, V. R. ., Seetha, J. ., Kumar, T. S. ., Kannaiah, S. K. ., J, B. ., & Brahmaiah, M. . (2023). Computer-Aided Detection of Skin Cancer Detection from Lesion Images via Deep-Learning Techniques. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2s), 293–302. https://doi.org/10.17762/ijritcc.v11i2s.615
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
