Abstract
Classification has huge applications in medical field starting from diagnosis, prognosis, or treatment outcome prediction. Classification task in medical field is not only simply related to accuracy but also reveals biological information and facts derived from classifier. Due to importance of classification task in health care, we have reviewed various classifiers on different datasets. The study presents a comparative performance analysis of various classifiers with the motive to select the classification algorithm that best classifies the data. All the classifier models are built and experiments are performed on different medical datasets using cross-validation technique. The performance analysis of these classifier models is validated using various metrics that include accuracy, precision, recall, and F1 score. The model that generates the best values for all these metrics is chosen as the best classifier. The experimental results obtained prove that the SVM (support vector machine) and the logistic regression classifier models gave the best and the most consistent results for all the chosen datasets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
R. Deo, Machine learning in medicine. Am. Heart Assoc. J. 132(20), 1920–1930 (2015)
D. Bhavani, A. Vasasvi, P. Keshava, Machine learning: a critical review of classification techniques. Intl. J. Adv. Res. Comp. Commun. Eng. ISSN: 2319-5940
O. Obaid, M. Mohammed, A. Ghani, S. Mostafa, F. Taha AL-Dhief, Evaluating the performance of machine learning techniques in the classification of Wisconsin breast cancer. Intl. J. Eng. Technol. (IJET) 7, 160–166 (2018)
C. Latha, S. Jeeva, Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques. Inform. Med. Unlocked 16, 100203 (2019)
M. Saritas, A. Yasar, Performance analysis of ANN and Naive Bayes classification algorithm for data classification. Intl. J. Intelligent Systems Appl. Eng 7, 88–91 (2019)
G. Rumbe, H. Youth, Comparative study of classification techniques on breast cancer FNA biopsy data. Intl. J. Artif. Intellig. Interact. Multim. 1, 5–12 (2010). https://doi.org/10.9781/ijimai.2010.131
A. Christobel, P. Sivaprakasam, Improving the performance of k-nearest neighbor algorithm for the classification of diabetes dataset with missing values. Intl. J. Comp. Eng. Technol. (IJCET) 7(3), 155–167 (2012)
I. Rish, An empirical study of the Naïve Bayes classifier. IJCAI 2001 Work Empir. Methods Artif. Intell. 3, 41–46 (2001)
R. Wang, AdaBoost for feature selection, classification and its relation with SVM. Intl. Conf. Solid State Devices Mat. Sci. 25, 800–807 (2012)
A. Wyner, M. Maolson, J. Bleich, Explaining the success of AdaBoost and random forests as interpolating classifiers. J. Mach. Learn. Res. 18 (2017)
J. Peng, C.-Y.J. Peng, K.L. Lee, G.M. Ingersoll, An introduction to logistic regression analysis and reporting. J. Educ. Res. 96, 3–14 (2002)
M. Gladence, M. Karthi, A. Maria, A statistical comparison of logistic regression and different Bayes classification methods for machine learning. ARPN J. Eng. Appl. Sci. 10, 5947–5953 (2015)
M. Amin, A. Ali, Performance Evaluation of Supervised Machine Learning Classifiers for Predicting Healthcare Operational Decisions (Department of Computer Science & Engineering University of Engineering & Technology, Lahore, 2019)
E. Zriqat, A. Altamimi, M. Azzeh, A comparative study for predicting heart diseases using data mining classification methods. Intl. J. Comp. Sci. Inf. Security (IJCSIS) 14(12), 869–879 (2017)
W. Shijun, R. Summers, Machine learning and radiology. Med. Image Anal. 16, 933–951 (2012). https://doi.org/10.1016/j.media.2012.02.005
D. Bansal, R. Chhikara, K. Khanna, P. Gupta, Comparative analysis of various machine learning algorithms for detecting dementia. Procedia Comput. Sci. 132, 1497–1502 (2018). https://doi.org/10.1016/j.procs.2018.05.102
G. Munjal, M. Hanmandlu, S. Srivastava, Novel gene selection method for breast cancer classification. J. Biochem. Technol. 8(4), 1116–1120
M. Lamba, G. Munjal, Y. Gigras, Feature selection of micro-array expression data (FSM) – a review. Proc. Comput. Sci. 132, 1619–1625 (2018)
C. Sidey-Gibbons, J. Sidey-Gibbons, Machine learning in medicine: a practical introduction. BMC Med. Res. Methodol. 19 (2019). https://doi.org/10.1186/s12874-019-0681-4
J. Anderson, J. Parikh, D. Shenfeld, Reverse engineering and evaluation of prediction models for progression to Type 2 diabetes: application of machine learning using electronic health records. J. Diabetes Sci. Technol. 10 (2016). https://doi.org/10.1177/1932296815620200
G. Bedi, F. Carrillo, G.A. Cecchi, D.F. Slezak, M. Sigman, N.B. Mota, S. Ribeiro, D.C. Javitt, M. Copelli, C.M. Corcoran, Automated analysis of free speech predicts psychosis onset in high-risk youths. Nat. Partner J. (NPJ) Schizophrenia 1(1), 15030 (2015)
Y. Yamada, M. Kobayashi, Detecting mental fatigue from eye-tracking data gathered while watching video: evaluation in younger and older adults. Artif. Intell. Med. 91 (2018). https://doi.org/10.1016/j.artmed.2018.06.005
Y. Liu, K. Gadepalli, M. Norouzi, G. Dahl, T. Kohlberger, A. Boyko, S. Venugopalan, A. Timofeev, P. Nelson, G. Corrado, J. Hipp, L. Peng, M. Stumpe. Detecting Cancer Metastases on Gigapixel Pathology Images (2017). arxiv:1703.02442.
N. Lutimath, C. Chethan, B.S. Pol, Prediction of heart disease using machine learning. Intl. J. Recent Technol. Eng. (IJRTE). ISSN: 2277-3878 8(2S10) (2019)
S. Vanaja, K. Rameshkumar, Performance analysis of classification algorithms on medical diagnoses-a survey. J. Comput. Sci. 11, 30–52 (2015)
G. Munjal, S. Kaur. Comparative study of ANN for pattern classification WSEAS. International Conference on Mathematical Methods and Computational Techniques in Electrical Engineering, Bucharest, Romania (2006)
R.L. Borges, Analysis of the Wisconsin Breast cancer dataset and machine learning for breast cancer detection. Proceedings of XI Workshop de Visao Computacional (October 5–7th, 2015)
M. Singaravelu, S. Rajapraksh, S. Krishnan, K. Karthik, Classification of liver patient dataset using machine learning algorithms. Intl. J. Eng. Technol. 7, 323–326 (2018). https://doi.org/10.14419/ijet.v7i3.34.19217
S. Abbas, R. Riaz, S. Kazmi, S. Rizvi, S.J. Kwon, Cause analysis of caesarian sections and application of machine learning methods for classification of birth data. IEEE 6, 67555–67561 (2018)
F. Sardouk, A. Duru, O. Bayat, Classification of breast cancer using data mining. Am. Sci. Res. J. Eng. Technol. Sci. (ASRJETS) 51(1), 38–46 (2019)
M. Rahman, N. Davis Darryl, Machine learning based missing value imputation method for clinical datasets. IAENG Trans. Eng. Technol. 229 (2012). https://doi.org/10.1007/978-94-007-6190-2_19
S. Kotsiantis, D. Kanellopoulos, P. Pintelas, Data preprocessing for supervised learning. Int. J. Comput. Sci. 1, 111–117 (2006)
F. Ahmed, Y. Ali, S. Shamsuddin, Using K-fold cross validation proposed models for Spikeprop learning enhancements. Intl. J. Eng. Technol. (UAE) 7, 145–151 (2018). https://doi.org/10.14419/ijet.v7i4.11.20790
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Malik, D., Munjal, G. (2021). Reviewing Classification Methods on Health Care. In: Bhatia, S., Dubey, A.K., Chhikara, R., Chaudhary, P., Kumar, A. (eds) Intelligent Healthcare. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-67051-1_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-67051-1_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-67050-4
Online ISBN: 978-3-030-67051-1
eBook Packages: EngineeringEngineering (R0)