A Review on Machine Learning Algorithms in Healthcare

Year 2022, Volume: 6 Issue: 2, 117 - 143, 31.12.2022

Lütviye Özge Polatlı , Melis Almula Karadayı

https://doi.org/10.52148/ehta.1117769

Abstract

In recent years, the issue of improving health processes by using machine learning algorithms by researchers has become a big trend. Machine learning has become a popular and effective method used to improve the quality of healthcare services, prevent disease outbreaks, diagnose diseases early, reduce hospital operating costs, assist the government in healthcare policies, and increase healthcare efficiency. In this review, machine learning studies carried out in the field of health are summarized and classified. In particular, the focus is on studies of non-communicable diseases, which threaten public health and are at the top of the list of causes of death in the world. In addition, the COVID-19 disease, which is on the list of the world's largest deadly diseases and has been declared a public health emergency in recent years, is also included. The purpose of this study is to assist researchers working in the field of health in choosing appropriate algorithms. As a result of the compilation studies, the best performing classification algorithm in healthcare services was Decision Tree(DT), Random Forest (RF), Gaussian Naive Bayes (GNB) with an average accuracy of 100%.

Keywords

Machine learning algorithms, healthcare, communicable diseases, non-communicable diseases

Sağlık Hizmetlerinde Güncel Makine Öğrenmesi Algoritmaları

Abstract

Son yıllarda araştırmacılar tarafından makine öğrenmesi algoritmalarını kullanarak sağlık süreçlerinin iyileştirilmesi konusu büyük bir trend haline gelmiştir. Makine öğrenmesi, sağlık hizmetlerinde kaliteyi yükseltmek, hastalık yayılımlarını önlemek, hastalıkları erken teşhis etmek, hastane operasyon maliyetlerini azaltmak, hükümete sağlık hizmetleri politikalarında yardımcı olmak ve sağlık hizmetinin verimliliğini artırmak için kullanılan popüler ve etkili bir yöntem haline gelmiştir. Bu derleme çalışmasında, sağlık alanında gerçekleştirilen makine öğrenmesi çalışmaları özetlenmiş ve sınıflandırılmıştır. Özellikle halk sağlığını tehdit eden ve dünyada ölüm nedenleri listesinde ilk sıralarda yer alan, bulaşıcı olmayan hastalık çalışmalarına odaklanılmıştır. Ayrıca dünyanın en büyük ölümcül hastalıklar listesinde yer alan ve son yıllarda halk sağlığı için acil durum ilan edilen COVID-19 hastalığına da yer verilmiştir. Bu çalışmanın amacı, sağlık alanında çalışma yapan araştırmacılara uygun algoritmalarını seçmesinde yardımcı olmaktır. Derleme çalışmasının sonucunda sağlık hizmetlerinde en iyi performans gösteren sınıflandırma algoritması ortalama %100 doğruluk başarısıyla Decision Tree (DT), Random Forest (RF), Gaussian Naive Bayes (GNB) olmuştur.

Keywords

Makine öğrenmesi algoritmaları, sağlık hizmetleri, bulaşıcı hastalıklar, bulaşıcı olmayan hastalıklar

References

• H. T. Melis Almula Karadayı, Beyza Özlem YILMAZ, Bilgehan Eren Erol, “Sağlık Teknolojisi Değerlendirmede Çok Kriterli Karar Verme Yaklaşımları Üzerine Bir Derleme Çalışması,” Düzce Üniversitesi Bilim ve Teknol. Derg., vol. 8, no. Mcdm, pp. 264–289, 2020.
• Z. T. Kalender, H. Tozan, and O. Vayvay, “Prioritization of medical errors in patient safety management: Framework using interval-valued intuitionistic fuzzy sets,” Healthc., vol. 8, no. 3, 2020, doi: 10.3390/healthcare8030265.
• M. A. KARADAYI, Y. G. GÖKMEN, L. G. KASAP, and H. TOZAN, “Sağlıkta Güncel Simülasyon Yaklaşımları: Bir Derleme Çalışması,” Int. J. Adv. Eng. Pure Sci., pp. 1–21, 2019, doi: 10.7240/jeps.444190.
• N. Öztürk, H. Tozan, and Ö. Vayvay, “A new decision model approach for health technology assessment and a case study for dialysis alternatives in Turkey,” Int. J. Environ. Res. Public Health, vol. 17, no. 10, 2020, doi: 10.3390/ijerph17103608.
• WHO, “the-Top-10-Causes-of-Death @ Www.Who.Int,” The top 10 causes of death. p. Consultado 23 de marzo de 2019, 2018, [Online]. Available: https://www.who.int/es/news-room/fact-sheets/detail/the-top-10-causes-of-death.
• M. Ferdous, J. Debnath, and N. R. Chakraborty, “Machine Learning Algorithms in Healthcare: A Literature Survey,” 2020 11th Int. Conf. Comput. Commun. Netw. Technol. ICCCNT 2020, 2020, doi: 10.1109/ICCCNT49239.2020.9225642.
• G. Winter, “Machine learning in healthcare,” Br. J. Heal. Care Manag., vol. 25, no. 2, pp. 100–101, 2019, doi: 10.12968/bjhc.2019.25.2.100.
• P. Sun, X. Lu, C. Xu, W. Sun, and B. Pan, “Understanding of COVID-19 based on current evidence,” J. Med. Virol., vol. 92, no. 6, pp. 548–551, 2020, doi: 10.1002/jmv.25722.
• P. Saha, M. S. Sadi, and M. M. Islam, “EMCNet: Automated COVID-19 diagnosis from X-ray images using convolutional neural network and ensemble of machine learning classifiers,” Informatics Med. Unlocked, vol. 22, p. 100505, 2021, doi: 10.1016/j.imu.2020.100505.
• M. Pourhomayoun and M. Shakibi, “Predicting mortality risk in patients with COVID-19 using machine learning to help medical decision-making,” Smart Heal., vol. 20, no. April 2020, p. 100178, 2021, doi: 10.1016/j.smhl.2020.100178.
• E. Gambhir, R. Jain, A. Gupta, and U. Tomer, “Regression Analysis of COVID-19 using Machine Learning Algorithms,” Proc. - Int. Conf. Smart Electron. Commun. ICOSEC 2020, no. Icosec, pp. 65–71, 2020, doi: 10.1109/ICOSEC49089.2020.9215356.
• V. Bayat et al., “A Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Prediction Model From Standard Laboratory Tests,” Clin. Infect. Dis., vol. 2, no. Xx Xxxx, pp. 1–7, 2020, doi: 10.1093/cid/ciaa1175.
• M. A. Alves et al., “Explaining machine learning based diagnosis of COVID-19 from routine blood tests with decision trees and criteria graphs,” Comput. Biol. Med., vol. 132, no. March, 2021, doi: 10.1016/j.compbiomed.2021.104335.
• P. S. Mung and S. Phyu, “Effective Analytics on Healthcare Big Data Using Ensemble Learning,” 2020 IEEE Conf. Comput. Appl. ICCA 2020, 2020, doi: 10.1109/ICCA49400.2020.9022853.
• H. Ahmed, E. M. G. Younis, A. Hendawi, and A. A. Ali, “Heart disease identification from patients’ social posts, machine learning solution on Spark,” Futur. Gener. Comput. Syst., vol. 111, pp. 714–722, 2020, doi: 10.1016/j.future.2019.09.056.
• K. Balaji, K. Lavanya, and A. G. Mary, “Machine learning algorithm for clustering of heart disease and chemoinformatics datasets,” Comput. Chem. Eng., vol. 143, p. 107068, 2020, doi: 10.1016/j.compchemeng.2020.107068.
• G. M. Sridhar and A. Prema Kirubakaran, “Heart disease and optimal prediction of attacks using hybrid machine learning algorithm: A survey,” Mater. Today Proc., no. xxxx, 2021, doi: 10.1016/j.matpr.2020.12.865.
• K. Arul Jothi, S. Subburam, V. Umadevi, and K. Hemavathy, “Heart disease prediction system using machine learning,” Mater. Today Proc., no. xxxx, pp. 1–3, 2021, doi: 10.1016/j.matpr.2020.12.901.
• S. Faiayaz Waris and S. Koteeswaran, “Heart disease early prediction using a novel machine learning method called improved K-means neighbor classifier in python,” Mater. Today Proc., no. xxxx, pp. 1–7, 2021, doi: 10.1016/j.matpr.2021.01.570.
• J. P. Li, A. U. Haq, S. U. Din, J. Khan, A. Khan, and A. Saboor, “Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare,” IEEE Access, vol. 8, no. Ml, pp. 107562–107582, 2020, doi: 10.1109/ACCESS.2020.3001149.
• B. P. Nguyen et al., “Predicting the onset of type 2 diabetes using wide and deep learning with electronic health records,” Comput. Methods Programs Biomed., vol. 182, no. August, 2019, doi: 10.1016/j.cmpb.2019.105055.
• N. P. Tigga and S. Garg, “Prediction of Type 2 Diabetes using Machine Learning Classification Methods,” Procedia Comput. Sci., vol. 167, no. 2019, pp. 706–716, 2020, doi: 10.1016/j.procs.2020.03.336.
• R. B. Lukmanto, Suharjito, A. Nugroho, and H. Akbar, “Early detection of diabetes mellitus using feature selection and fuzzy support vector machine,” Procedia Comput. Sci., vol. 157, pp. 46–54, 2019, doi: 10.1016/j.procs.2019.08.140.
• A. Viloria, Y. Herazo-Beltran, D. Cabrera, and O. B. Pineda, “Diabetes Diagnostic Prediction Using Vector Support Machines,” Procedia Comput. Sci., vol. 170, pp. 376–381, 2020, doi: 10.1016/j.procs.2020.03.065.
• B. M. K. P, S. P. R, N. R K, and A. K, “Type 2: Diabetes mellitus prediction using Deep Neural Networks classifier,” Int. J. Cogn. Comput. Eng., vol. 1, no. July, pp. 55–61, 2020, doi: 10.1016/j.ijcce.2020.10.002.
• D. Jashwanth Reddy et al., “Predictive machine learning model for early detection and analysis of diabetes,” Mater. Today Proc., no. xxxx, 2020, doi: 10.1016/j.matpr.2020.09.522.
• A. Mujumdar and V. Vaidehi, “Diabetes Prediction using Machine Learning Algorithms,” Procedia Comput. Sci., vol. 165, pp. 292–299, 2019, doi: 10.1016/j.procs.2020.01.047.
• S. Grampurohit and C. Sagarnal, “Disease prediction using machine learning algorithms,” 2020 Int. Conf. Emerg. Technol. INCET 2020, no. November, 2020, doi: 10.1109/INCET49848.2020.9154130.
• N. Öztürk, H. Tozan, and Ö. Vayvay, “Comprehensive Needs Analysis For Health Technology Assessment Studies and Improvement Proposal,” vol. 1, no. 1, pp. 69–76, 2016.
• I. Gatos et al., “A Machine-Learning Algorithm Toward Color Analysis for Chronic Liver Disease Classification, Employing Ultrasound Shear Wave Elastography,” Ultrasound Med. Biol., vol. 43, no. 9, pp. 1797–1810, 2017, doi: 10.1016/j.ultrasmedbio.2017.05.002.
• V. J. Gogi and M. M. Vijayalakshmi, “Prognosis of Liver Disease: Using Machine Learning Algorithms,” 2018 Int. Conf. Recent Innov. Electr. Electron. Commun. Eng. ICRIEECE 2018, pp. 875–879, 2018, doi: 10.1109/ICRIEECE44171.2018.9008482.
• C. C. Wu et al., “Prediction of fatty liver disease using machine learning algorithms,” Comput. Methods Programs Biomed., vol. 170, pp. 23–29, 2019, doi: 10.1016/j.cmpb.2018.12.032.
• S. Thaiparnit, N. Chumuang, and M. Ketcham, “A Comparitive Study of Clasification Liver Dysfunction with Machine Learning,” 2018 Int. Jt. Symp. Artif. Intell. Nat. Lang. Process. iSAI-NLP 2018 - Proc., vol. 283, pp. 1–4, 2018, doi: 10.1109/iSAI-NLP.2018.8692808.
• S. Shi et al., “Using Machine Learning to Predict Postoperative Liver Dysfunction After Aortic Arch Surgery,” J. Cardiothorac. Vasc. Anesth., vol. 000, 2021, doi: 10.1053/j.jvca.2021.02.046.
• M. Srivenkatesh, “Performance Evolution of Different Machine Learning Algorithms for Prediction of Liver Disease,” Int. J. Innov. Technol. Explor. Eng., vol. 9, no. 2, pp. 1115–1122, 2019, doi: 10.35940/ijitee.l3619.129219.
• J. P. Sarkar, I. Saha, A. Sarkar, and U. Maulik, “Machine learning integrated ensemble of feature selection methods followed by survival analysis for predicting breast cancer subtype specific miRNA biomarkers,” Comput. Biol. Med., vol. 131, no. January, p. 104244, 2021, doi: 10.1016/j.compbiomed.2021.104244.
• V. N. Gopal, F. Al-Turjman, R. Kumar, L. Anand, and M. Rajesh, “Feature Selection and Classification in Breast Cancer Prediction using IoT and Machine Learning,” Measurement, vol. 178, no. February, p. 109442, 2021, doi: 10.1016/j.measurement.2021.109442.
• J. Wu and C. Hicks, “Breast cancer type classification using machine learning,” J. Pers. Med., vol. 11, no. 2, pp. 1–12, 2021, doi: 10.3390/jpm11020061.
• H. Asri, H. Mousannif, H. Al Moatassime, and T. Noel, “Using Machine Learning Algorithms for Breast Cancer Risk Prediction and Diagnosis,” Procedia Comput. Sci., vol. 83, no. Fams, pp. 1064–1069, 2016, doi: 10.1016/j.procs.2016.04.224.
• B. Karthikeyan, S. Gollamudi, H. V. Singamsetty, P. K. Gade, and S. Y. Mekala, “Breast cancer detection using machine learning,” Int. J. Adv. Trends Comput. Sci. Eng., vol. 9, no. 2, pp. 981–984, 2020, doi: 10.30534/ijatcse/2020/12922020.
• N. Al-Azzam and I. Shatnawi, “Comparing supervised and semi-supervised Machine Learning Models on Diagnosing Breast Cancer,” Ann. Med. Surg., vol. 62, no. December 2020, pp. 53–64, 2021, doi: 10.1016/j.amsu.2020.12.043.
• A. R. Vaka, B. Soni, and S. R. K., “Breast cancer detection by leveraging Machine Learning,” ICT Express, vol. 6, no. 4, pp. 320–324, 2020, doi: 10.1016/j.icte.2020.04.009.
• D. H. Abd and I. S. Al-Mejibli, “Monitoring System for Sickle Cell Disease Patients by Using Supervised Machine Learning,” 2017 2nd Al-Sadiq Int. Conf. Multidiscip. IT Commun. Sci. Appl. AIC-MITCSA 2017, pp. 119–124, 2017, doi: 10.1109/AIC-MITCSA.2017.8723006.
• R. Chen, J. Krejza, M. Arkuszewski, R. A. Zimmerman, E. H. Herskovits, and E. R. Melhem, “Brain morphometric analysis predicts decline of intelligence quotient in children with sickle cell disease: A preliminary study,” Adv. Med. Sci., vol. 62, no. 1, pp. 151–157, 2017, doi: 10.1016/j.advms.2016.09.002.
• D. Abd, J. K. Alwan, M. Ibrahim, and M. B. Naeem, “The utilisation of machine learning approaches for medical data classification and personal care system mangementfor sickle cell disease,” 2017 Annu. Conf. New Trends Inf. Commun. Technol. Appl. NTICT 2017, no. March, pp. 213–218, 2017, doi: 10.1109/NTICT.2017.7976147.
• M. Khalaf et al., “The utilisation of composite machine learning models for the classification of medical datasets for sickle cell disease,” 2016 6th Int. Conf. Digit. Inf. Process. Commun. ICDIPC 2016, pp. 37–41, 2016, doi: 10.1109/ICDIPC.2016.7470788.
• U. Chauhan, V. Kumar, V. Chauhan, S. Tiwary, and A. Kumar, “Cardiac Arrest Prediction using Machine Learning Algorithms,” 2019 2nd Int. Conf. Intell. Comput. Instrum. Control Technol. ICICICT 2019, no. Cvd, pp. 886–890, 2019, doi: 10.1109/ICICICT46008.2019.8993296.
• H. K. Chang et al., “Early detecting in-hospital cardiac arrest based on machine learning on imbalanced data,” 2019 IEEE Int. Conf. Healthc. Informatics, ICHI 2019, pp. 1–10, 2019, doi: 10.1109/ICHI.2019.8904504.
• Y. Hirano, Y. Kondo, K. Sueyoshi, K. Okamoto, and H. Tanaka, “Early outcome prediction for out-of-hospital cardiac arrest with initial shockable rhythm using machine learning models,” Resuscitation, vol. 158, no. August, pp. 49–56, 2021, doi: 10.1016/j.resuscitation.2020.11.020.
• M. Safa and A. Pandian, “Applying machine learning algorithm to sensor coupled IoT devices in prediction of cardiac stress – An integrated approach,” Mater. Today Proc., no. xxxx, 2021, doi: 10.1016/j.matpr.2021.02.698.
• J. myoung Kwon et al., “Deep-learning-based out-of-hospital cardiac arrest prognostic system to predict clinical outcomes,” Resuscitation, vol. 139, no. March 2019, pp. 84–91, 2019, doi: 10.1016/j.resuscitation.2019.04.007.
• S. Layeghian Javan, M. M. Sepehri, M. Layeghian Javan, and T. Khatibi, “An intelligent warning model for early prediction of cardiac arrest in sepsis patients,” Comput. Methods Programs Biomed., vol. 178, pp. 47–58, 2019, doi: 10.1016/j.cmpb.2019.06.010.
• Sanjeev Kumar SunilaGodara, “Prediction of Thyroid Disease Using Machine Learning Techniques,” vol. 10, no. 2, pp. 787–793, 2018.
• A. Tyagi, R. Mehra, and A. Saxena, “Interactive thyroid disease prediction system using machine learning technique,” PDGC 2018 - 2018 5th Int. Conf. Parallel, Distrib. Grid Comput., pp. 689–693, 2018, doi: 10.1109/PDGC.2018.8745910.
• K. Pavya and B. Srinivasan, “Feature selection algorithms to improve thyroid disease diagnosis,” IEEE Int. Conf. Innov. Green Energy Healthc. Technol. - 2017, IGEHT 2017, pp. 1–5, 2017, doi: 10.1109/IGEHT.2017.8094070.
• S. O. Olatunji et al., “Early diagnosis of thyroid cancer diseases using com putational intelligence techniques: A case study of a Saudi Arabian dataset,” Comput. Biol. Med., vol. 131, no. February, p. 104267, 2021, doi: 10.1016/j.compbiomed.2021.104267.
• P. Lodha, A. Talele, and K. Degaonkar, “Diagnosis of Alzheimer’s Disease Using Machine Learning,” Proc. - 2018 4th Int. Conf. Comput. Commun. Control Autom. ICCUBEA 2018, pp. 1–4, 2018, doi: 10.1109/ICCUBEA.2018.8697386.
• J.Neelaveni and Geetha Devasana, "Alzaeimer Disease Prediction using Machine Learning Algorithm", 6th International Conference on Advanced Computing & Communication Systems (I.C.A.C.C.S.), 2020
• F. Zhang, S. Tian, S. Chen, Y. Ma, X. Li, and X. Guo, “Voxel-Based Morphometry: Improving the Diagnosis of Alzheimer’s Disease Based on an Extreme Learning Machine Method from the ADNI cohort,” Neuroscience, vol. 414, pp. 273–279, 2019, doi: 10.1016/j.neuroscience.2019.05.014.
• A. Khan and S. Zubair, “An Improved Multi-Modal based Machine Learning Approach for the Prognosis of Alzheimer’s disease,” J. King Saud Univ. - Comput. Inf. Sci., no. xxxx, 2020, doi: 10.1016/j.jksuci.2020.04.004.
• U. Rangaswamy, S. A. P. Dharshini, D. Yesudhas, and M. M. Gromiha, “VEPAD - Predicting the effect of variants associated with Alzheimer’s disease using machine learning,” Comput. Biol. Med., vol. 124, no. July, p. 103933, 2020, doi: 10.1016/j.compbiomed.2020.103933.
• G. Uysal and M. Ozturk, “Hippocampal atrophy based Alzheimer’s disease diagnosis via machine learning methods,” J. Neurosci. Methods, vol. 337, no. February, p. 108669, 2020, doi: 10.1016/j.jneumeth.2020.108669.
• E. Ryzhikova et al., “Raman spectroscopy and machine learning for biomedical applications: Alzheimer’s disease diagnosis based on the analysis of cerebrospinal fluid,” Spectrochim. Acta - Part A Mol. Biomol. Spectrosc., vol. 248, p. 119188, 2021, doi: 10.1016/j.saa.2020.119188.
• S. Y. Yashfi et al., “Risk Prediction of Chronic Kidney Disease Using Machine Learning Algorithms,” 2020 11th Int. Conf. Comput. Commun. Netw. Technol. ICCCNT 2020, 2020, doi: 10.1109/ICCCNT49239.2020.9225548.
• Z. L. He et al., “Application of machine learning models for predicting acute kidney injury following donation after cardiac death liver transplantation,” Hepatobiliary Pancreat. Dis. Int., no. xxxx, pp. 1–10, 2021, doi: 10.1016/j.hbpd.2021.02.001.
• A. K. M. S. A. Rabby, R. Mamata, M. A. Laboni, Ohidujjaman, and S. Abujar, “Machine Learning Applied to Kidney Disease Prediction: Comparison Study,” 2019 10th Int. Conf. Comput. Commun. Netw. Technol. ICCCNT 2019, pp. 1–7, 2019, doi: 10.1109/ICCCNT45670.2019.8944799.
• N. A. Almansour et al., “Neural network and support vector machine for the prediction of chronic kidney disease: A comparative study,” Comput. Biol. Med., vol. 109, no. April, pp. 101–111, 2019, doi: 10.1016/j.compbiomed.2019.04.017.
• H. Tozan, M. Karatas, and O. Vayvay, “Reducing demand signal variability via a quantitative fuzzy grey regression approach,” Teh. Vjesn., vol. 25, no. September, pp. 411–419, 2018, doi: 10.17559/TV-20171115130250.