{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T11:19:30Z","timestamp":1768821570218,"version":"3.49.0"},"reference-count":44,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,4,15]],"date-time":"2022-04-15T00:00:00Z","timestamp":1649980800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Decision support systems with machine learning can help organizations improve operations and lower costs with more precision and efficiency. This work presents a review of state-of-the-art machine learning algorithms for binary classification and makes a comparison of the related metrics between them with their application to a public diabetes and human resource datasets. The two mainly used categories that allow the learning process without requiring explicit programming are supervised and unsupervised learning. For that, we use Scikit-learn, the free software machine learning library for Python language. The best-performing algorithm was Random Forest for supervised learning, while in unsupervised clustering techniques, Balanced Iterative Reducing and Clustering Using Hierarchies and Spectral Clustering algorithms presented the best results. The experimental evaluation shows that the application of unsupervised clustering algorithms does not translate into better results than with supervised algorithms. However, the application of unsupervised clustering algorithms, as the preprocessing of the supervised techniques, can translate into a boost of performance.<\/jats:p>","DOI":"10.3390\/a15040130","type":"journal-article","created":{"date-parts":[[2022,4,16]],"date-time":"2022-04-16T07:42:41Z","timestamp":1650094961000},"page":"130","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Machine Learning Algorithms: An Experimental Evaluation for Decision Support Systems"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1184-2433","authenticated-orcid":false,"given":"Hugo","family":"Silva","sequence":"first","affiliation":[{"name":"Polytechnic of Coimbra, Institute of Engineering of Coimbra\u2014ISEC, Rua Pedro Nunes, 3030-199 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9660-2011","authenticated-orcid":false,"given":"Jorge","family":"Bernardino","sequence":"additional","affiliation":[{"name":"Polytechnic of Coimbra, Institute of Engineering of Coimbra\u2014ISEC, Rua Pedro Nunes, 3030-199 Coimbra, Portugal"},{"name":"Centre for Informatics and Systems, University of Coimbra (CISUC), P\u00f3lo II, Pinhal de Marrocos, 3030-290 Coimbra, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bhardwaj, R., Nambiar, A.R., and Dutta, D. (2017, January 4\u20138). A Study of Machine Learning in Healthcare. Proceedings of the 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC), Turin, Italy.","DOI":"10.1109\/COMPSAC.2017.164"},{"key":"ref_2","unstructured":"IBM (2021, November 14). What is Machine Learning?. Available online: https:\/\/www.ibm.com\/cloud\/learn\/machine-learning."},{"key":"ref_3","unstructured":"Expert.ai (2021, November 14). What is the Definition of Machine Learning?. Available online: https:\/\/www.expert.ai\/blog\/machine-learning-definition\/."},{"key":"ref_4","unstructured":"Seema Singh (2021, November 14). An Introduction to Clustering. Clustering is Considered to be the Most\u2026 Data Driven Investor., Available online: https:\/\/medium.datadriveninvestor.com\/an-introduction-to-clustering-61f6930e3e0b."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1136\/svn-2017-000101","article-title":"Artificial intelligence in healthcare: Past, present and future","volume":"2","author":"Jiang","year":"2017","journal-title":"Stroke Vasc. Neurol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Uddin, S., Khan, A., Hossain, E., and Moni, M.A. (2019). Comparing different supervised machine learning algorithms for disease prediction. BMC Med. Inform. Decis. Mak., 19.","DOI":"10.1186\/s12911-019-1004-8"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Sidey-Gibbons, J.A.M., and Sidey-Gibbons, C.J. (2019). Machine learning in medicine: A practical introduction. BMC Med. Res. Methodol., 19.","DOI":"10.1186\/s12874-019-0681-4"},{"key":"ref_8","first-page":"888","article-title":"Using Electronic Health Records and Machine Learning to Predict Postpartum Depression","volume":"264","author":"Wang","year":"2019","journal-title":"Stud. Health Technol. Inform."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ferdous, M., Debnath, J., and Chakraborty, N.R. (2020, January 1\u20133). Machine Learning Algorithms in Healthcare: A Literature Survey. Proceedings of the 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kharagpur, India.","DOI":"10.1109\/ICCCNT49239.2020.9225642"},{"key":"ref_10","first-page":"318","article-title":"Improving Mechanical Ventilator Clinical Decision Support Systems with a Machine Learning Classifier for Determining Ventilator Mode","volume":"264","author":"Rehm","year":"2019","journal-title":"Stud. Health Technol. Inform."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.ijmedinf.2017.11.010","article-title":"Evaluation of three machine learning models for self-referral decision support on low back pain in primary care","volume":"110","author":"Poel","year":"2018","journal-title":"Int. J. Med. Inform."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Farhadian, M., Shokouhi, P., and Torkzaban, P. (2020). A decision support system based on support vector machine for diagnosis of periodontal disease. BMC Res. Notes, 13.","DOI":"10.1186\/s13104-020-05180-5"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"626697","DOI":"10.3389\/fpubh.2021.626697","article-title":"Machine Learning Based Clinical Decision Support System for Early COVID-19 Mortality Prediction","volume":"9","author":"Karthikeyan","year":"2021","journal-title":"Front. Public Health"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1291","DOI":"10.1016\/j.cmi.2020.02.003","article-title":"Machine learning in infection management using routine electronic health records: Tools, techniques, and reporting of future technologies","volume":"26","author":"Luz","year":"2020","journal-title":"Clin. Microbiol. Infect."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1111\/nyas.13218","article-title":"Machine learning approaches to personalize early prediction of asthma exacerbations","volume":"1387","author":"Finkelstein","year":"2017","journal-title":"Ann. N. Y. Acad. Sci."},{"key":"ref_16","unstructured":"von Luxburg, U., Williamson, R.C., and Guyon, I. (2012, January 2). Clustering: Science or Art?. Proceedings of the ICML Workshop on Unsupervised and Transfer Learning, Bellevue, WA, USA."},{"key":"ref_17","unstructured":"Fu, T., and Zhang, Z. (2017, January 9\u201311). CPSG-MCMC: Clustering-Based Preprocessing method for Stochastic Gradient MCMC. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, FL, USA."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yoon, K., and Kwek, S. (2005, January 6\u20139). An unsupervised learning approach to resolving the data imbalanced issue in supervised learning problems in functional genomics. Proceedings of the Fifth International Conference on Hybrid Intelligent Systems (HIS\u201905), Rio de Janeiro, Brazil.","DOI":"10.1109\/ICHIS.2005.23"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1029\/2018WR023160","article-title":"A clustering preprocessing framework for the subannual calibration of a hydrological model considering climate-land surface variations\u2013Enhanced Reader","volume":"54","author":"Lan","year":"2018","journal-title":"Water Resour. Res."},{"key":"ref_20","unstructured":"IBM (2021, December 26). What is Logistic Regression?. Available online: https:\/\/www.ibm.com\/se-en\/topics\/logistic-regression."},{"key":"ref_21","unstructured":"Gandhi, R., and Towards Data Science (2021, December 26). Support Vector Machine\u2014Introduction to Machine Learning Algorithms. Available online: https:\/\/towardsdatascience.com\/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47."},{"key":"ref_22","unstructured":"(2021, December 26). What Is a Decision Tree?. Available online: https:\/\/www.mastersindatascience.org\/learning\/introduction-to-machine-learning-algorithms\/decision-tree\/."},{"key":"ref_23","unstructured":"Gandhi, R., and Towards Data Science (2021, December 26). Naive Bayes Classifier. What is a Classifier?. Available online: https:\/\/towardsdatascience.com\/naive-bayes-classifier-81d512f50a7c."},{"key":"ref_24","unstructured":"IBM (2021, December 26). What is Random Forest?. Available online: https:\/\/www.ibm.com\/cloud\/learn\/random-forest."},{"key":"ref_25","unstructured":"(2021, December 26). What Is K-Nearest Neighbor? An ML Algorithm to Classify Data. Available online: https:\/\/learn.g2.com\/k-nearest-neighbor."},{"key":"ref_26","unstructured":"Garbade, M.J., and Towards Data Science (2021, December 26). Understanding K-means Clustering in Machine Learning. Available online: https:\/\/towardsdatascience.com\/understanding-k-means-clustering-in-machine-learning-6a6e67336aa1."},{"key":"ref_27","unstructured":"(2021, December 26). What is Spectral Clustering and How its Work?. Available online: https:\/\/www.mygreatlearning.com\/blog\/introduction-to-spectral-clustering\/."},{"key":"ref_28","unstructured":"(2021, December 26). Mean Shift. Available online: https:\/\/ml-explained.com\/blog\/mean-shift-explained."},{"key":"ref_29","unstructured":"do Prado, K.S., and Towards Data Science (2021, December 26). How DBSCAN Works and Why Should We Use it?. Available online: https:\/\/towardsdatascience.com\/how-dbscan-works-and-why-should-i-use-it-443b4a191c80."},{"key":"ref_30","unstructured":"(2021, December 26). BIRCH Clustering Clearly Explained. Available online: https:\/\/morioh.com\/p\/c23e0d680669."},{"key":"ref_31","unstructured":"Gupta, A., and Geek Culture|Medium (2021, December 26). Balanced Iterative Reducing and Clustering Using Hierarchies (BIRCH) Algorithm in Machine Learning. Available online: https:\/\/medium.com\/geekculture\/balanced-iterative-reducing-and-clustering-using-hierarchies-birch-1428bb06bb38."},{"key":"ref_32","unstructured":"Kaggle (2021, December 27). Pima Indians Diabetes Database. Available online: https:\/\/www.kaggle.com\/uciml\/pima-indians-diabetes-database."},{"key":"ref_33","unstructured":"Britannica (2021, December 27). Pima|People. Available online: https:\/\/www.britannica.com\/topic\/Pima-people."},{"key":"ref_34","first-page":"79","article-title":"Impact of Data Normalization on Classification Model Accuracy","volume":"27","author":"Borkin","year":"2019","journal-title":"Res. Pap. Fac. Mater. Sci. Technol. Slovak Univ. Technol."},{"key":"ref_35","unstructured":"(2022, February 07). Hr-Comma-Sep. Kaggle. Available online: https:\/\/www.kaggle.com\/pankeshpatel\/hrcommasep."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"469","DOI":"10.2298\/PSI170615023P","article-title":"Analyzing data from memory tasks-comparison of ANOVA, logistic regression and mixed logit model","volume":"51","author":"Mihic","year":"2018","journal-title":"Psihologija"},{"key":"ref_37","unstructured":"(2022, February 05). Sklearn.Svm.SVC\u2014Scikit-Learn 1.0.2 Documentation. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.svm.SVC.html."},{"key":"ref_38","unstructured":"(2022, February 05). Sklearn.Ensemble.RandomForestClassifier\u2014Scikit-Learn 1.0.2 Documentation. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.ensemble.RandomForestClassifier.html."},{"key":"ref_39","unstructured":"(2022, February 05). Sklearn.Neighbors.KNeighborsClassifier\u2014Scikit-Learn 1.0.2 Documentation. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.neighbors.KNeighborsClassifier.html."},{"key":"ref_40","unstructured":"(2022, February 05). Sklearn.Cluster.KMeans\u2014Scikit-Learn 1.0.2 Documentation. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.cluster.KMeans.html."},{"key":"ref_41","unstructured":"(2022, February 05). Sklearn.Cluster.SpectralClustering\u2014Scikit-Learn 1.0.2 Documentation. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.cluster.SpectralClustering.html."},{"key":"ref_42","unstructured":"(2022, February 05). Sklearn.Cluster.MeanShift\u2014Scikit-Learn 1.0.2 Documentation. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.cluster.MeanShift.html."},{"key":"ref_43","unstructured":"(2022, February 05). Sklearn.Cluster.DBSCAN\u2014Scikit-Learn 1.0.2 Documentation. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.cluster.DBSCAN.html."},{"key":"ref_44","unstructured":"(2022, February 05). Sklearn.Cluster.Birch\u2014Scikit-Learn 1.0.2 Documentation. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.cluster.Birch.html."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/15\/4\/130\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:55:01Z","timestamp":1760136901000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/15\/4\/130"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,15]]},"references-count":44,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["a15040130"],"URL":"https:\/\/doi.org\/10.3390\/a15040130","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,15]]}}}