OPTYMALIZACJA DRZEWA DECYZYJNEGO OPARTA NA ALGORYTMIE GENETYCZNYM DO WYKRYWANIA DEMENCJI POPRZEZ ANALIZĘ MRI

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.
DOI: https://doi.org/10.1007/s42044-022-00100-1   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.
DOI: https://doi.org/10.3233/JAD-2005-7301   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.
DOI: https://doi.org/10.1016/j.cmpb.2023.107745   Google Scholar

Angelillo M. T. et al.: Attentional pattern classification for automatic dementia detection. IEEE Access 7, 2019, 57706–57716.
DOI: https://doi.org/10.1109/ACCESS.2019.2913685   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.
DOI: https://doi.org/10.1016/j.procs.2018.05.102   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.
DOI: https://doi.org/10.1109/ACCESS.2021.3066213   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.
DOI: https://doi.org/10.1038/s41598-022-11731-6   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.
DOI: https://doi.org/10.1016/j.ins.2021.11.052   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.
DOI: https://doi.org/10.3390/su14031447   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.
DOI: https://doi.org/10.3389/fnagi.2021.708932   Google Scholar

Drouka A. et al.: Dietary and nutrient patterns and brain MRI biomarkers in dementia-free adults. Nutrients 14(11), 2022, 2345.
DOI: https://doi.org/10.3390/nu14112345   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.
DOI: https://doi.org/10.1016/j.jiph.2022.06.008   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.
DOI: https://doi.org/10.20517/ais.2021.15   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.
DOI: https://doi.org/10.1016/j.compbiomed.2022.106404   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.
DOI: https://doi.org/10.1016/j.patcog.2021.108434   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.
DOI: https://doi.org/10.1007/s11517-022-02630-z   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.
DOI: https://doi.org/10.1016/j.neuroscience.2015.08.013   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.
DOI: https://doi.org/10.1056/NEJMra2302038   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.
DOI: https://doi.org/10.1007/s10916-023-01906-7   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.
DOI: https://doi.org/10.1007/s00415-022-11518-9   Google Scholar

Liang X. et al.: Evaluating voice-assistant commands for dementia detection. Computer Speech & Language 72, 2022, 101297.
DOI: https://doi.org/10.1016/j.csl.2021.101297   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.
DOI: https://doi.org/10.1016/j.jbi.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.
DOI: https://doi.org/10.1016/j.asoc.2023.110680   Google Scholar

Miled Z. B. et al.: Feature engineering from medical notes: A case study of dementia detection. Heliyon 9(3), 2023.
DOI: https://doi.org/10.1016/j.heliyon.2023.e14636   Google Scholar

Mirheidari B. et al.: Dementia detection using automatic analysis of conversations. Computer Speech & Language 53, 2019, 65–79.
DOI: https://doi.org/10.1016/j.csl.2018.07.006   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.
DOI: https://doi.org/10.1016/j.bspc.2021.103293   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.
DOI: https://doi.org/10.1162/jocn.2009.21407   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.
DOI: https://doi.org/10.1016/j.trci.2019.10.006   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.
DOI: https://doi.org/10.3389/fnagi.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.
DOI: https://doi.org/10.1016/j.egyr.2022.01.046   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.
DOI: https://doi.org/10.1016/j.compbiomed.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.
DOI: https://doi.org/10.3390/app7070651   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.
DOI: https://doi.org/10.1016/j.eswa.2021.116464   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.
DOI: https://doi.org/10.1038/s41746-022-00592-y   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.
DOI: https://doi.org/10.3389/fpsyt.2022.899729   Google Scholar