GENETIC ALGORITHM-BASED DECISION TREE OPTIMIZATION FOR DETECTION OF DEMENTIA THROUGH MRI ANALYSIS

Govada Anuradha


Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering (India)
https://orcid.org/0000-0002-0999-0376

Harini Davu

davuharini@gmail.com
Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering (India)
https://orcid.org/0009-0008-6187-1797

Muthyalanaidu Karri


Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering (India)
https://orcid.org/0009-0006-5850-3761

Abstract

Dementia is a devastating neurological disorder that affects millions of people globally, causing progressive decline in cognitive function and daily living activities. Early and precise detection of dementia is critical for optimal dementia therapy and management however, the diagnosis of dementia is often challenging due to the complexity of the disease and the wide range of symptoms that patients may exhibit. Machine learning approaches are becoming progressively more prevalent in the realm of image processing, particularly for disease prediction. These algorithms can learn to recognize distinctive characteristics and patterns that are suggestive of specific diseases by analyzing images from multiple medical imaging modalities. This paper aims to develop and optimize a decision tree algorithm for dementia detection using the OASIS dataset, which comprises a large collection of MRI images and associated clinical data. This approach involves using a genetic algorithm to optimize the decision tree model for maximum accuracy and effectiveness. The ultimate goal of the paper is to develop an effective, non-invasive diagnostic tool for early and accurate detection of dementia. The GA-based decision tree, as proposed, exhibits strong performance compared to alternative models, boasting an impressive accuracy rate of 96.67% according to experimental results.


Keywords:

dementia, genetic algorithm, decision tree

Abdollahi J., Nouri-Moghaddam B.: Hybrid stacked ensemble combined with genetic algorithms for diabetes prediction. Iran Journal of Computer Science 5(3), 2022, 205–220.
  Google Scholar

Adeli H., Ghosh-Dastidar S., Dadmehr N.: Alzheimer's disease and models of computation: Imaging, classification, and neural models. Journal of Alzheimer's Disease 7(3), 2005, 187–199.
  Google Scholar

Al-Badarneh A., Najadat H., Alraziqi A.: Brain Images Classifier: A Hybrid Approach Using Decision Trees and Genetic Algorithms. JINT 7(2), 2016.
  Google Scholar

Aminizadeh S. et al.: The applications of machine learning techniques in medical data processing based on distributed computing and the Internet of Things. Computer methods and programs in biomedicine 241, 2023, 107745.
  Google Scholar

Angelillo M. T. et al.: Attentional pattern classification for automatic dementia detection. IEEE Access 7, 2019, 57706–57716.
  Google Scholar

Azad R. et al.: Medical image segmentation on MRI images with missing modalities: a review [http://arxiv.org/abs/2203.06217].
  Google Scholar

Bansal D. et al.: Comparative analysis of various machine learning algorithms for detecting dementia. Procedia computer science 132, 2018, 1497–1502.
  Google Scholar

Basheer S., Bhatia S., Sakri S. B.: Computational modeling of dementia prediction using deep neural network: analysis on OASIS dataset. IEEE access 9, 2021, 42449–42462.
  Google Scholar

Biswal A.: What Is Principal Component Analysis? Simplilearn.com [www.simplilearn.com/tutorials/machine-learning-tutorial/principal-component-analysis] (avaible 7.11.2023).
  Google Scholar

Bukhari S. N. H., Webber J., Mehbodniya A.: Decision tree based ensemble machine learning model for the prediction of Zika virus T-cell epitopes as potential vaccine candidates. Scientific Reports 12(1), 2022, 7810.
  Google Scholar

Deng W. et al.: An enhanced fast non-dominated solution sorting genetic algorithm for multi-objective problems. Information Sciences 585, 2022, 441–453.
  Google Scholar

Dhiman G. et al.: A novel machine-learning-based hybrid CNN model for tumor identification in medical image processing. Sustainability 14(3), 2022, 1447.
  Google Scholar

Díaz-Álvarez J. et al.: Genetic algorithms for optimized diagnosis of Alzheimer’s disease and Frontotemporal dementia using Fluorodeoxyglucose positron emission tomography imaging. Frontiers in aging neuroscience 13, 2022, 983.
  Google Scholar

Drouka A. et al.: Dietary and nutrient patterns and brain MRI biomarkers in dementia-free adults. Nutrients 14(11), 2022, 2345.
  Google Scholar

Elhazmi A. et al.: Machine learning decision tree algorithm role for predicting mortality in critically ill adult COVID-19 patients admitted to the ICU. Journal of infection and public health 15(7), 2022, 826–834.
  Google Scholar

Elyan E. et al.: Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward. Artificial Intelligence Surgery 2, 2022.
  Google Scholar

Emam M. M., Houssein E. H., Ghoniem R. M.: A modified reptile search algorithm for global optimization and image segmentation: Case study brain MRI images. Computers in Biology and Medicine 152, 2023, 106404.
  Google Scholar

Fang L., Wang X.: Brain tumor segmentation based on the dual-path network of multi-modal MRI images. Pattern Recognition 124, 2022, 108434.
  Google Scholar

García-Gutierrez F. et al.: GA-MADRID: Design and validation of a machine learning tool for the diagnosis of Alzheimer’s disease and frontotemporal dementia using genetic algorithms. Medical & Biological Engineering & Computing 60(9), 2022, 2737–2756.
  Google Scholar

Gorji H. T., Haddadnia J.: A novel method for early diagnosis of Alzheimer’s disease based on pseudo Zernike moment from structural MRI. Neuroscience 305, 2015, 361–371.
  Google Scholar

Haug C. J., Drazen J. M.: Artificial intelligence and machine learning in clinical medicine. New England Journal of Medicine 388(13), 2023, 1201–1208.
  Google Scholar

Javeed A. et al.: Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions. Journal of Medical Systems 47(1), 2023, 17.
  Google Scholar

Leocadi M. et al.: Awareness impairment in Alzheimer’s disease and fronto-temporal dementia: a systematic MRI review. Journal of Neurology 270(4), 2023, 1880–1907.
  Google Scholar

Liang X. et al.: Evaluating voice-assistant commands for dementia detection. Computer Speech & Language 72, 2022, 101297.
  Google Scholar

Li R. et al.: Applications of artificial intelligence to aid early detection of dementia: a scoping review on current capabilities and future directions. Journal of biomedical informatics 127, 2022, 104030.
  Google Scholar

Liu H. et al.: NeuroCrossover: An intelligent genetic locus selection scheme for genetic algorithm using reinforcement learning. Applied Soft Computing 146, 2023, 110680.
  Google Scholar

Miled Z. B. et al.: Feature engineering from medical notes: A case study of dementia detection. Heliyon 9(3), 2023.
  Google Scholar

Mirheidari B. et al.: Dementia detection using automatic analysis of conversations. Computer Speech & Language 53, 2019, 65–79.
  Google Scholar

Mirzaei G., Adeli H.: Machine learning techniques for diagnosis of Alzheimer disease, mild cognitive disorder, and other types of dementia. Biomedical Signal Processing and Control 72, 2022, 103293.
  Google Scholar

Marcus D. S. et al.: Open access series of imaging studies: longitudinal MRI data in nondemented and demented older adults. Journal of Cognitive Neuroscience 22(12), 2010, 2677–2684.
  Google Scholar

Nori V. S. et al.: Machine learning models to predict onset of dementia: a label learning approach. Alzheimer's & Dementia: Translational Research & Clinical Interventions 5, 2019, 918–925.
  Google Scholar

Nowroozpoor A. et al.: Detecting cognitive impairment and dementia in the emergency department: a scoping review. Journal of the American Medical Directors Association 23(8), 2022, 1314–1315.
  Google Scholar

Perovnik M. et al.: Automated differential diagnosis of dementia syndromes using FDG PET and machine learning. Frontiers in Aging Neuroscience 14, 2022, 1005731.
  Google Scholar

Ramos D. et al.: Using decision tree to select forecasting algorithms in distinct electricity consumption context of an office building. Energy Reports 8, 2022, 417–422.
  Google Scholar

Shehab M. et al.: Machine learning in medical applications: A review of state-of-the-art methods. Computers in Biology and Medicine 145, 2022, 105458.
  Google Scholar

So A. et al.: Early diagnosis of dementia from clinical data by machine learning techniques. Applied Sciences 7(7), 2017, 651.
  Google Scholar

Squires M. et al.: A novel genetic algorithm based system for the scheduling of medical treatments. Expert Systems with Applications 195, 2022, 116464.
  Google Scholar

Varoquaux G. Cheplygina V.: Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ Digital Medicine 5(1), 2022, 48.
  Google Scholar

World Health Organization: WHO and World Health Organization: WHO. Dementia [www.who.int/news-room/fact-sheets/detail/dementia/?gclid=CjwKCAiA-P-rBhBEEiwAQEXhHzn09E8kOoRAOoS8xNltu1svCep3MzGBPB363AJ20n3XF3v9M9C9axoCS7QQAvD_BwE] (avaible 15.03.2023).
  Google Scholar

Zhao X. et al.: A voice recognition-based digital cognitive screener for dementia detection in the community: Development and validation study. Frontiers in Psychiatry 13, 2022, 899729.
  Google Scholar

Download


Published
2024-03-31

Cited by

Anuradha, G., Davu, H., & Karri, M. (2024). GENETIC ALGORITHM-BASED DECISION TREE OPTIMIZATION FOR DETECTION OF DEMENTIA THROUGH MRI ANALYSIS. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 14(1), 83–89. https://doi.org/10.35784/iapgos.5775

Authors

Govada Anuradha 

Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering India
https://orcid.org/0000-0002-0999-0376

Authors

Harini Davu 
davuharini@gmail.com
Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering India
https://orcid.org/0009-0008-6187-1797

Authors

Muthyalanaidu Karri 

Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering India
https://orcid.org/0009-0006-5850-3761

Statistics

Abstract views: 176
PDF downloads: 125


License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.