Mathematical Model of Disease Progression and Application of Machine Learning Algorithms for Predicting Disease Stages
Journal of Disease and Global Health, Volume 16, Issue 2,
Accurately diagnosing a patient's disease stage is critical to deal with the varying symptoms and applying appropriate therapeutics at different stages. Therefore, we used and compared machine learning algorithms on synthetic data and classified disease stages. This study was performed on the Google Colab environment with Python's machine learning libraries (Sklearn). The simulated data resembled one that might be reasonably seen in real patients' data since it contained both temporal variation and various noise levels. Three types of machine learning algorithms were used: Nearest Neighbor, Decision Tree, and Neural Network. These algorithms could classify whether the current stage of disease progression was at its early, mid, or late stages under different noise levels and other time intervals. The neural network algorithm showed the best performance. Although this study used a synthetic data set, it demonstrated how machine learning could be applied in the medical field to enhance patient care.
- Disease progression model
- disease stage prediction
- google colab environment
- machine learning algorithms
- neural network
How to Cite
Arias CR. An introduction to artificial intelligence. SPU Works. 2022;173.
Awad M, Khanna R. Machine learning. In: Efficient learning machines. Berkeley, CA: Apress; 2015.
Jason B. ’Supervised and unsupervised machine learning algorithms’, Machine Learning Algorithms; 2016.
Sarker IH. Machine learning: Algorithms, real-world applications and research directions. SN Comput Sci. 2021;2(3): 160.
Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O et al. Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. J Big Data. 2021;8(1): 53.
Charu CA, Chandan KR. Data clustering algorithms and applications. Minneapolis: University of Minnesota, Department of Computer Science and Engineering, by Taylor & Francis Group. LLC; 2014.
Janiesch C, Zschech P, Heinrich K. Machine learning and deep learning. Electron Markets. 2021;31(3):685-95.
Hugo JE, Eduardo FM, “Chapter 5 - Dimensionality reduction” Alejandro AT-G, Carlos AR-G, Luis V-P, Omar M-M, editors. Biosignal Processing and Classification Using Computational Learning and Intelligence. Academic Press; 2022.
Sarker IH, Learning D. Deep Learning: A comprehensive overview on techniques, taxonomy, applications and research directions. SN Comput Sci. 2021;2(6):420.
Dwivedi YK, Ismagilova E, Hughes DL, Carlson J, Filieri R, Jacobson J et al. Setting the future of digital and social media marketing research: perspectives and research propositions. Int J Inf Manag. 2021;59.
Pinto MF, Oliveira H, Batista S, Cruz L, Pinto M, Correia I et al. Prediction of disease progression and outcomes in multiple sclerosis with machine learning. Sci Rep. 2020;10(1):21038.
Ghasemi N, Razavi S, Nikzad E. Multiple sclerosis: pathogenesis, symptoms, diagnoses and cell-based therapy. Cell J. 2017;19(1):1-10.
Braley TJ, Chervin RD. Fatigue in multiple sclerosis: Mechanisms, evaluation, and treatment. Sleep. 2010;33(8):1061-7.
Christogianni A, Bibb R, Davis SL, Jay O, Barnett M, Evangelou N, Filingeri D. Temperature sensitivity in multiple sclerosis: An overview of its impact on sensory and cognitive symptoms. Temperature (Austin). 2018;5(3):208-223.
Mohd J, Abid H, Ravi PS, Rajiv S, Shanay R. Significance of machine learning in healthcare: features, pillars and applications. Int J Intell Netw. 2020;3: 58-73.
Fisher CK, Smith AM, Walsh JR, Coalition Against Major Diseases, Abbott, Alliance for Aging Research, Alzheimer’s Association, Alzheimer’s Foundation of America, AstraZeneca Pharmaceuticals LP, Bristol-Myers Squibb Company, Critical Path Institute, CHDI Foundation, Inc., Eli Lilly and Company, F. Hoffmann-La Roche Ltd, Forest Research Institute, Genentech, Inc., GlaxoSmithKline, Johnson & Johnson, National Health Council, Novartis Pharmaceuticals Corporation, Parkinson’s Action Network, Parkinson’s Disease Foundation, Pfizer, Inc., sanofi-aventis. Collaborating Organi-zations: Clinical Data Interchange Standards Consortium (CDISC), Ephibian, Metrum Institute. ’Machine learning for comprehensive forecasting of Alzheimer’s Disease progression,’ Science Report. Sci Rep. 2019;9(1):13622.
NIH. Urinary incontinence in older adults; n.d. National Institute on Aging, NIH. Available: https://www.nia.nih.gov/health/urinary-incontinence-older-adults
Mnih V, Larochelle H, Hinton G. Conditional restricted Boltzmann machines for structured output prediction. Proceedings of the twenty-seventh conference on uncertainty in artificial intelligence, Barcelona, Spain, Conference: UAI, July 14-17; 2011.
Lee HW, Sung JJY, Ahn SH. Artificial intelligence in liver disease. J Gastroenterol Hepatol. 2021;36(3):539-42.
Khullar V, Firpi RJ. Hepatitis C cirrhosis: New perspectives for diagnosis and treatment. World J Hepatol. 2015;7(14):1843-55.
Quiroz-Juárez MA, Torres-Gómez A, Hoyo-Ulloa I, León-Montiel RJ, U’Ren AB. Identification of high-risk COVID-19 patients using machine learning. Plos One. 2021;16(9):e0257234..
Konerman MA, Zhang Y, Zhu J, Higgins PD, Lok AS, Waljee AK. Improvement of predictive models of the risk of disease progression in chronic hepatitis C by incorporating longitudinal data. Hepatology. 2015;61(6):1832-41.
Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial networks." arXiv:1406.2661. Commun ACM. 2014;63 (11):139-44.
Konerman MA, Beste LA, Van T, Liu B, Zhang X, Zhu J et al. Machine learning models to predict disease progression among veterans with hepatitis C virus. Plos One. 2019;14(1):e0208141.
Harvey D, Valkenburg W, Amara A. Predicting malaria epidemics in Burkina Faso with machine learning. Plos One. 2021;16(6):e0253302.
Abdul Salam M, Taha S, Ramadan M. COVID-19 detection using federated machine learning. Plos One. 2021;16(6): e0252573.
Christie SA, Conroy AS, Callcut RA, Hubbard AE. Cohen MJ. Multi-outcome prediction after injury: applying adaptive machine learning for precision medicine in trauma. Plos One. 2019;14(4):e0213836.
Gardiner LJ, Carrieri AP, Bingham K, Macluskie G, Bunton D, McNeil M, et al. Combining explainable machine learning, demographic and multi-omic data to inform precision medicine strategies for inflammatory bowel disease. Plos One. 2022;17(2):e0263248.
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