Abstract
In recent years, the data processed by the healthcare domain is increasing at an unprecedented rate accompanied by rich knowledge for medical research but low information has led to Healthcare Analytics. Healthcare Analytics collects the data from a myriad of areas such as clinicians, hospitals, government agencies, health insurance, pharmaceutical, and biotechnology agencies and allows for the examination of trends and patterns in various healthcare data. Based on this pattern, healthcare analytics determines how healthcare can be upgraded while constraining exorbitant spending. The retrieval of a pattern from healthcare data pertinent to the healthcare application and input to the Machine Learning algorithm is decided based on the data features. Feature selection approaches are developed to choose a subset of features that explain the details to achieve a more appropriate and compact depiction of the knowledge available. This paper summarizes Feature Selection algorithms and presents the challenges involved in healthcare data and also present an abstract architecture of data analytics in healthcare domain. Various algorithms and techniques in machine learning are compared and classified based on the applications of Healthcare Analytics.
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References
Zhao, Y., Liu, L., Qi, Y., Lou, F., Zhang, J., Ma, W.: Evaluation and design of public health information management system for primary health care units based on medical and health information. In: JIPH 2019. Elsevier (2019)
Khalifa, M., Zabani, I.: Utilizing health analytics in improving the performance of healthcare services. J. Infect. Public Health 9(6), 757–765 (2016)
Maqbool, D., Chambers, C.: Healthcare Analytics, Essentials of Business Analytics. ISORMS, vol. 264. Springer Nature Switzerland AG (2019)
Alkhatib, M.A., Talaei-Khoei, A., Ghapanchi, A.H.: Analysis of research in healthcare data analytics. In: Australian Conference on Information Systems (2015)
Yang, J., et al.: Emerging information technologies for enhanced healthcare. Comput. Ind. 2015(69), 3–11 (2015)
Baldwin, J.L., Singh, H., Sittig, D.F., Giardina, T.D.: Patient portals and health apps: pitfalls, promises, and what one might learn from the other. Healthcare 5(3), 81–85 (2016)
Hu, J., Adam, P., Wang, F.: Data Driven Analytics for Personalized Healhcare, & Information Management Systems: Cases, Strategies, & Solutions, Health Informatics. Springer, Switzerland (2016)
Kupwade, P.H., Seshadri, R.: Big data security and privacy ıssues in healthcare. In: IEEE International Congress on Big Data (2014)
Belle, A., Thiagarajan, R., Soroushmehr, S.M.R., Navidi, F., Beard, D.A., Najarian, K.: Big Data Analytics in Healthcare, pp. 1–16. Hindawi Publ. Corporation, London (2016)
Asante-Korang, A., Jacobs, J.P.: Big Data and pediatric cardiovascular disease in the era of transparency in healthcare. Cardiol. Young 26, 1597–1602 (2016)
Simpao, A.F., Ahumada, L.M., Galvez, J.A., Rehman, M.A.: A review of analytics and clinical informatics in healthcare. J. Med. Syst. 38(4), 45 (2014)
Chandola, V., Sukumar, S.R., Schryver, J.: Knowledge discovery from massive healthcare claims data. In: KDD. Association for Computing Machinery (2013)
Kuo, M.H., Sahama, T., Kushnirul, A.W., Borycki, E.M., Grunwell, D.K.: Health big data analytics: current perspectives, challenges & potential solutions. IJBDI 1(1–2), 114–126 (2014)
Raghupathi, W., Raghupathi, V.: Big data analytics in healthcare: promise and potential. Health Inf. Sci. Syst. 2(1), 3 (2014)
Dhayne, H., Haque, R., Kilany, R., Taher, Y.: In Search of Big Medical Data Integration Solutions-A Comprehensive Survey, IEEE (2019)
Luo, J., Wu, M., Gopukumar, D., Zhao, Y.: Big Data Application in Biomedical Research and healthcare: A Literature Review, Biomedical Informatics Insights (2016)
Yakout, M., Elmagarmid, A.K., Neville, J.: Ranking for Data Repairs. Cyber Centre Publications (2010)
International Review of Data Quality. Health Information & Quality Authority. (2011). Accessed from http://www.hiqa.ie/publications/international-review-data-quality
Kechadi, M.-T.: Healthcare big data: challenges and opportunities. In: BDAW and Advanced Wireless Technologies, pp. l–6 (2016)
Jagadish, H.V., et al.: Big data and ıts technical challenges. Commun. ACM 57(7), 86–94 (2014)
Dong, X.L., Naumann, F.: Data fusion. VLDB Endowment, 1654–1655 (2009)
Shortliffe, E.H., Sondik, E.J.: The public health informatics infrastructure: anticipation its role in cancer. Cancer Causes Control 17, 861–869 (2006)
Ingram, S., Munzner, T., Irvine, V., Tory, M., Bergner, S., Moller, T.: DimStiller: workflows for dimensional analysis and reduction. In: IEEE (2010)
Wang, L., Wang, G., Alexander, C.A.: Big data and visualization: methods, challenges and technology progress. Dig. Technol. 1(1), 33–38 (2015)
Brownson, R., Baker, E., Leet, T., Gillespie, K., True, W.: Evidence-Based Public Health. Oxford University Press, New York (2011)
Fang, R., Pouyanfar, S., Yang, Y., Chen, S.-C., Iyengar, S.S.: Computational health ınformatics in big data age: a survey. ACM Comput. Surv. (CSUR) 49(1), 1–36 (2016)
Sofia, Batra, I., Verma, V., Malik, A.: A comprehensive analysis of data collection methods in ınternet of things. In: ICAICR 2019. ACM (2019)
Tippet, A.: Data capture and analytics in healthcare (2014). http://blogs.zebra.com/data-capture-analytics-in-healthcare
NetApp. EHR solutions: Efficient, high-availability EHR data storage & management (2011). http://www.netapp.com/us/system/pdf-reader.aspx?cc=us&m=ds-3222.pdf&pdfUri=tcm:10-61401
Moshe, L.: UCI machine learning repository (2013). http://archive.ics.uci.edu/ml
Crowne, A.: Preparing healthcare industry to capture the full potential of big data (2014). http://sparkblog.emc.com/2014/06/preparing-healthcare-industry-capture-full-potential-big-data
Dong, X., Li, R., He, H., Zhou, W., Xue, Z., Wu, H.: Secure sensitive data sharing on a big data platform. Tsinghua Sci. Technol. 20(1), 72–80 (2015)
Written, I.H., Frank, E., Hall, M., Mark, A.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. Elsevier (2011)
Rajiv, R.S., Sailesh, C., Rahul, B.: An approach for real-time stress-trend detection using physiological signals wearable computing systems for automotive drivers. In: IEEE, pp. 1477–1482 (2011)
Daby, S., Deepak, S.T., Michael, S.: Mining of sensor data in healthcare: a survey. In: Managing and Mining Sensor Data, pp. 459–504. Springer (2013)
Anthony, S.F., et al.: Harrisons Principles of Internal Medicine, vol. 2. McGraw-Hill Medical, New York (2008)
Apiletti, D., Baralis, E., Bruno, G., Cerquitell, T.: Real-time analysis of physiological data to support medical applications. IEEE Trans. Inf Technol. Biomed. 13(3), 313–321 (2009)
Christos A.F., et al.: On the classification of emotional biosignals evoked while viewing effective pictures: an integrated data-mining-based approach healthcare applications. IEEE, 309–318 (2010)
Hu, F., Jiang, M., Celentano, L., Xiao, Y.: Robust medical ad hoc sensor networks with wavelet-based ECG data mining. Ad Hoc Netw. 6(7), 986–1012 (2008)
Leema, A., Hemalatha, M.: An effective and adaptive data cleaning technique for colossal RFID data sets in healthcare. WSEAS 8, 243–252 (2011)
Andr’e, S.F., et al.: Data mining using clinical physiology at discharge to predict ICU readmissions. Expert Syst. Appl., 13158–13165 (2012)
Visalakshi, S., Radha, V.: A literature review of feature selection techniques and applications: review of feature selection in data mining. IEEE (2014)
Wassan, J., Wang, H., Zheng, H.: Machine learning in bioinformatics. In: Encyclopedia of Bioinformatics and Computational Biology, pp. 300–308 (2018)
Leach, K.N.: A survey paper on ındependent component analysis. IEEE, 239–242 (2002)
Van Do, L., Anh, D.T.: Some ımprovements for time series subsequence join based on pearson correlation coefficients. In: SoICT, pp. 58–65. ACM (2016)
Yan, H., Dai, Y.: The comparison of five discriminant methods. In: International Conference on Management and Service Science. IEEE (2011)
Vanaja, S., Rameshkumar, K.: Analysis of feature selection algorithms on classification: a survey. Int. J. Comput. Appl. 96(17), 28–35 (2014)
Ruggieri, S.: Efficient c4.5. IEEE Trans. KDE 14(2), 438–444 (2002)
Dawy, Z., Michel, S., Joachim, H., Jakob, C.M.: Fine scale genetic mapping using ındependent component analysis. IEEE ACM (TCBB) 5(3), 448–460 (2008)
Taban, E., Muaaz Gul, A., Fahad, S.: A GPU based technique to compute pairwise Pearson’s correlation coefficients big fMRI data. In: ACM-BCB, pp. 723–728 (2017)
Varatharajan, R., Gunasekaran, M., Priyan, M.: Big data classification approach using LDA with enhanced SVM method ECG signals in cloud computing, pp. 10195–10215. Springer (2017)
Singhal, V., Singh, P.: Correlation based feature selection for diagnosis of acute lymphoblastic leukemia. ACM, 5–9 (2015)
Lee, S.-J., Xu, Z., Li, T., Yang, Y.: A novel bagging C4.5 algorithm based on wrapper feature selection for supporting wise clinical decision making. JBI 78, 1–30 (2017)
Athmaja, S., Hanumanthappa, M., Kavitha, V.: A survey of Machine Learning algorithms for big data analytics. In: ICIIECS (2017)
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)
Qui, J., Wu, Q., Ding, G., Xu, Y., Feng: A survey of machine learning for big data processing. In: EURASIP, pp. 1–16. Springer (2016)
Younas, K., Usman, Q., Nazish, Y., Aimal, K.: Machine learning techniques for heart disease datasets: a survey. In: ICMLC, pp. 27–35 (2019)
Clayton, P.D., Hripcsak, G.: Decision support in healthcare. Int. J. Biomed. Comput. 39, 59–66 (1995)
Chi, C.-L., et al.: Medical Decision Support Systems based on Machine learning. University of Lowa. http://ir.uiowa.edu/cgi/viewcontent.cgi/article=1469&context=etd
Patsaraporn, S.: Forecasting Dengue fever epidemics using ARIMA model. ACM (2019)
Chris, M., Caramanis, C., Shie, M., Sanjay, S.: Detecting epidemics using highly noisy data. In: MobiHoc. Association for Computing Machinery, ACM (2013)
Krzysztof, M.: Informed Mutation Operator using Machine Learning for Optimization in Epidemics Prevention. Association for Computing Machinery, ACM (2019)
Saberian, F., Zamani, A., Gooya, M.M., Hemmanti, P., Shooredeli, M.A., Teshnehlab, M.: Prediction of seasonal Influenza epidemics in Tehran using artificial neural networks. In: ICEE (2014)
Bai, X., Song, W., Chen, J.: Ebola prediction with epidemic model. IEEE (2016)
Alam, R., et al.: Motion biomarkers for early detection of dementia-related agitation. ACM Dig. Biomarkers (2017)
Appukuttan, A., Sindhu, L.: Curvelet & PNN classifier based approach for early detection & classification of breast cancer in digital mammograms. In: ICICT 2016 (2016)
Paredes, S., Rocha, T., de Carvalho, P., Henriques, J., Morais, J., Mendes, M.: Cardiovascular disease risk assessment innovative approaches developed in Heart Cycle project. In: International Conference of the IEEE EMBC (2013)
Ran, W., Tian, X., Wang, Y.: Health risk assessment model of drinking water Sources based on Bayesian & Triangle Fuzzy Number. In: ICAIP. ACM (2019)
Shankar, M., Pahadia, M., Srivastava, D., Ashwin, T.S., Reddy, G.R.M.: A novel method for disease recognition and cure time prediction based on symptoms. In: ICCCE (2015)
Chen, M., Zhao, X.: Fatty liver disease prediction based on multi-layer random forest model. In: CSAI, China Association for Computing Machinery. ACM (2018)
Golam, M., Qasim, F., Ripon, S.H.: Tuberous Sclerosis Complex (TSC) disease prediction using optimisized convolutional neural network. In: ICCCM. ACM (2019)
Wu, A.Y., Munteanu, C.: Understanding older users’ acceptance of wearable interfaces for sensor-based fall risk assessment. In: CHI, pp. 1–13 (2018)
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Veena, A., Gowrishankar, S. (2021). Healthcare Analytics: Overcoming the Barriers to Health Information Using Machine Learning Algorithms. In: Chen, J.IZ., Tavares, J.M.R.S., Shakya, S., Iliyasu, A.M. (eds) Image Processing and Capsule Networks. ICIPCN 2020. Advances in Intelligent Systems and Computing, vol 1200. Springer, Cham. https://doi.org/10.1007/978-3-030-51859-2_44
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