IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i22p2970-d684285.html
   My bibliography  Save this article

Machine Learning (ML) in Medicine: Review, Applications, and Challenges

Author

Listed:
  • Amir Masoud Rahmani

    (Future Technology Research Center, National Yunlin University of Science and Technology, Douliou 64002, Taiwan
    Amir Masoud Rahmani and Amir Haider have contributed equally to this work.)

  • Efat Yousefpoor

    (Department of Computer Engineering, Dezful Branch, Islamic Azad University, Dezful 73210, Iran)

  • Mohammad Sadegh Yousefpoor

    (Department of Computer Engineering, Dezful Branch, Islamic Azad University, Dezful 73210, Iran)

  • Zahid Mehmood

    (Department of Computer Engineering, University of Engineering and Technology, Taxila 47050, Pakistan)

  • Amir Haider

    (School of Intelligent Mechatronics Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea
    Amir Masoud Rahmani and Amir Haider have contributed equally to this work.)

  • Mehdi Hosseinzadeh

    (Pattern Recognition and Machine Learning Lab, Gachon University, 1342 Seongnamdaero, Sujeanggu, Seongnam 13120, Korea)

  • Rizwan Ali Naqvi

    (School of Intelligent Mechatronics Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea)

Abstract

Today, artificial intelligence (AI) and machine learning (ML) have dramatically advanced in various industries, especially medicine. AI describes computational programs that mimic and simulate human intelligence, for example, a person’s behavior in solving problems or his ability for learning. Furthermore, ML is a subset of artificial intelligence. It extracts patterns from raw data automatically. The purpose of this paper is to help researchers gain a proper understanding of machine learning and its applications in healthcare. In this paper, we first present a classification of machine learning-based schemes in healthcare. According to our proposed taxonomy, machine learning-based schemes in healthcare are categorized based on data pre-processing methods (data cleaning methods, data reduction methods), learning methods (unsupervised learning, supervised learning, semi-supervised learning, and reinforcement learning), evaluation methods (simulation-based evaluation and practical implementation-based evaluation in real environment) and applications (diagnosis, treatment). According to our proposed classification, we review some studies presented in machine learning applications for healthcare. We believe that this review paper helps researchers to familiarize themselves with the newest research on ML applications in medicine, recognize their challenges and limitations in this area, and identify future research directions.

Suggested Citation

  • Amir Masoud Rahmani & Efat Yousefpoor & Mohammad Sadegh Yousefpoor & Zahid Mehmood & Amir Haider & Mehdi Hosseinzadeh & Rizwan Ali Naqvi, 2021. "Machine Learning (ML) in Medicine: Review, Applications, and Challenges," Mathematics, MDPI, vol. 9(22), pages 1-52, November.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:22:p:2970-:d:684285
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/22/2970/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/9/22/2970/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gergo Pinter & Imre Felde & Amir Mosavi & Pedram Ghamisi & Richard Gloaguen, 2020. "COVID-19 Pandemic Prediction for Hungary; A Hybrid Machine Learning Approach," Mathematics, MDPI, vol. 8(6), pages 1-20, June.
    2. Amir Mosavi & Pedram Ghamisi & Yaser Faghan & Puhong Duan, 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," Papers 2004.01509, arXiv.org.
    3. Saeed Nosratabadi & Amir Mosavi & Puhong Duan & Pedram Ghamisi, 2020. "Data Science in Economics," Papers 2003.13422, arXiv.org.
    4. Shrestha, Yash Raj & Krishna, Vaibhav & von Krogh, Georg, 2021. "Augmenting organizational decision-making with deep learning algorithms: Principles, promises, and challenges," Journal of Business Research, Elsevier, vol. 123(C), pages 588-603.
    5. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," MetaArXiv haf2v, Center for Open Science.
    6. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," LawArXiv kczj5, Center for Open Science.
    7. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," SocArXiv 9vdwf, Center for Open Science.
    8. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," OSF Preprints yc6e2, Center for Open Science.
    9. Stetco, Adrian & Dinmohammadi, Fateme & Zhao, Xingyu & Robu, Valentin & Flynn, David & Barnes, Mike & Keane, John & Nenadic, Goran, 2019. "Machine learning methods for wind turbine condition monitoring: A review," Renewable Energy, Elsevier, vol. 133(C), pages 620-635.
    10. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," Thesis Commons auyvc, Center for Open Science.
    11. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," EdArXiv 5dwrt, Center for Open Science.
    12. Mosavi, Amir & Faghan, Yaser & Ghamisi, Pedram & Duan, Puhong & Ardabili, Sina Faizollahzadeh & Hassan, Salwana & Band, Shahab S., 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," OSF Preprints jrc58, Center for Open Science.
    13. Fen Yang & Hossein Moayedi & Amir Mosavi, 2021. "Predicting the Degree of Dissolved Oxygen Using Three Types of Multi-Layer Perceptron-Based Artificial Neural Networks," Sustainability, MDPI, vol. 13(17), pages 1-20, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Eugenio Vera-Salmerón & Carmen Domínguez-Nogueira & José L. Romero-Béjar & José A. Sáez & Emilio Mota-Romero, 2022. "Decision-Tree-Based Approach for Pressure Ulcer Risk Assessment in Immobilized Patients," IJERPH, MDPI, vol. 19(18), pages 1-9, September.
    2. Muhammad Umair Khan & Mehdi Hosseinzadeh & Amir Mosavi, 2022. "An Intersection-Based Routing Scheme Using Q-Learning in Vehicular Ad Hoc Networks for Traffic Management in the Intelligent Transportation System," Mathematics, MDPI, vol. 10(20), pages 1-25, October.
    3. Juan Laborda & Sonia Ruano & Ignacio Zamanillo, 2023. "Multi-Country and Multi-Horizon GDP Forecasting Using Temporal Fusion Transformers," Mathematics, MDPI, vol. 11(12), pages 1-26, June.
    4. Jan Lansky & Saqib Ali & Amir Masoud Rahmani & Mohammad Sadegh Yousefpoor & Efat Yousefpoor & Faheem Khan & Mehdi Hosseinzadeh, 2022. "Reinforcement Learning-Based Routing Protocols in Flying Ad Hoc Networks (FANET): A Review," Mathematics, MDPI, vol. 10(16), pages 1-60, August.
    5. Amir Masoud Rahmani & Rizwan Ali Naqvi & Efat Yousefpoor & Mohammad Sadegh Yousefpoor & Omed Hassan Ahmed & Mehdi Hosseinzadeh & Kamran Siddique, 2022. "A Q-Learning and Fuzzy Logic-Based Hierarchical Routing Scheme in the Intelligent Transportation System for Smart Cities," Mathematics, MDPI, vol. 10(22), pages 1-31, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mei-Li Shen & Cheng-Feng Lee & Hsiou-Hsiang Liu & Po-Yin Chang & Cheng-Hong Yang, 2021. "An Effective Hybrid Approach for Forecasting Currency Exchange Rates," Sustainability, MDPI, vol. 13(5), pages 1-29, March.
    2. Petr Suler & Zuzana Rowland & Tomas Krulicky, 2021. "Evaluation of the Accuracy of Machine Learning Predictions of the Czech Republic’s Exports to the China," JRFM, MDPI, vol. 14(2), pages 1-30, February.
    3. Yong-Chao Su & Cheng-Yu Wu & Cheng-Hong Yang & Bo-Sheng Li & Sin-Hua Moi & Yu-Da Lin, 2021. "Machine Learning Data Imputation and Prediction of Foraging Group Size in a Kleptoparasitic Spider," Mathematics, MDPI, vol. 9(4), pages 1-16, February.
    4. Urko Aguirre-Larracoechea & Cruz E. Borges, 2021. "Imputation for Repeated Bounded Outcome Data: Statistical and Machine-Learning Approaches," Mathematics, MDPI, vol. 9(17), pages 1-27, August.
    5. Marcus Vinicius Santos & Fernando Morgado-Dias & Thiago C. Silva, 2023. "Oil Sector and Sentiment Analysis—A Review," Energies, MDPI, vol. 16(12), pages 1-29, June.
    6. ErLe Du & Meng Ji, 2021. "Analyzing the regional economic changes in a high-tech industrial development zone using machine learning algorithms," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-18, June.
    7. David G. Green, 2023. "Emergence in complex networks of simple agents," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 18(3), pages 419-462, July.
    8. Lin, Yong & Wang, Renyu & Gong, Xingyue & Jia, Guozhu, 2022. "Cross-correlation and forecast impact of public attention on USD/CNY exchange rate: Evidence from Baidu Index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    9. Steve J. Bickley & Benno Torgler, 2021. "Behavioural Economics, What Have we Missed? Exploring “Classical” Behavioural Economics Roots in AI, Cognitive Psychology, and Complexity Theory," CREMA Working Paper Series 2021-21, Center for Research in Economics, Management and the Arts (CREMA).
    10. Oliver Hümbelin & Lukas Hobi & Robert Fluder, 2021. "Rich Cities, Poor Countryside? Social Structure of the Poor and Poverty Risks in Urban and Rural Places in an Affluent Country. An Administrative Data based Analysis using Random Forest," University of Bern Social Sciences Working Papers 40, University of Bern, Department of Social Sciences, revised 10 Nov 2021.
    11. Saeed Nosratabadi & Nesrine Khazami & Marwa Ben Abdallah & Zoltan Lackner & Shahab S. Band & Amir Mosavi & Csaba Mako, 2020. "Social Capital Contributions to Food Security: A Comprehensive Literature Review," Papers 2012.03606, arXiv.org.
    12. Cheng Zhang & Nilam Nur Amir Sjarif & Roslina Ibrahim, 2023. "Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020-2022," Papers 2305.04811, arXiv.org, revised Sep 2023.
    13. Xiaodong Zhang & Suhui Liu & Xin Zheng, 2021. "Stock Price Movement Prediction Based on a Deep Factorization Machine and the Attention Mechanism," Mathematics, MDPI, vol. 9(8), pages 1-21, April.
    14. Teddy Lazebnik & Tzach Fleischer & Amit Yaniv-Rosenfeld, 2023. "Benchmarking Biologically-Inspired Automatic Machine Learning for Economic Tasks," Sustainability, MDPI, vol. 15(14), pages 1-9, July.
    15. Meir Russ, 2021. "Knowledge Management for Sustainable Development in the Era of Continuously Accelerating Technological Revolutions: A Framework and Models," Sustainability, MDPI, vol. 13(6), pages 1-32, March.
    16. Labib Shami & Teddy Lazebnik, 2024. "Implementing Machine Learning Methods in Estimating the Size of the Non-observed Economy," Computational Economics, Springer;Society for Computational Economics, vol. 63(4), pages 1459-1476, April.
    17. Ren, Yi-Shuai & Ma, Chao-Qun & Kong, Xiao-Lin & Baltas, Konstantinos & Zureigat, Qasim, 2022. "Past, present, and future of the application of machine learning in cryptocurrency research," Research in International Business and Finance, Elsevier, vol. 63(C).
    18. Cui, Xiwen & Yu, Xiaoyu & Niu, Dongxiao, 2024. "The ultra-short-term wind power point-interval forecasting model based on improved variational mode decomposition and bidirectional gated recurrent unit improved by improved sparrow search algorithm a," Energy, Elsevier, vol. 288(C).
    19. Di Wu & Zhenning Xu & Seung Bach, 2023. "Using Google Trends to predict and forecast avocado sales," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 629-641, December.
    20. Liang She & Jianyuan Wang & Yifan Bo & Yangyan Zeng, 2022. "MACA: Multi-Agent with Credit Assignment for Computation Offloading in Smart Parks Monitoring," Mathematics, MDPI, vol. 10(23), pages 1-18, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:9:y:2021:i:22:p:2970-:d:684285. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.