Login Register Cart 0
Generic placeholder image

International Journal of Sensors, Wireless Communications and Control

Editor-in-Chief

ISSN (Print): 2210-3279
ISSN (Online): 2210-3287

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Review Article

Exploring the Applications of Machine Learning in Healthcare

Author(s): Tausifa Jan Saleem* and Mohammad Ahsan Chishti

Volume 10, Issue 4, 2020

Page: [458 - 472] Pages: 15

DOI: 10.2174/2210327910666191220103417

Price: $65

Abstract

The rapid progress in domains like machine learning, and big data has created plenty of opportunities in data-driven applications particularly healthcare. Incorporating machine intelligence in healthcare can result in breakthroughs like precise disease diagnosis, novel methods of treatment, remote healthcare monitoring, drug discovery, and curtailment in healthcare costs. The implementation of machine intelligence algorithms on the massive healthcare datasets is computationally expensive. However, consequential progress in computational power during recent years has facilitated the deployment of machine intelligence algorithms in healthcare applications. Motivated to explore these applications, this paper presents a review of research works dedicated to the implementation of machine learning on healthcare datasets. The studies that were conducted have been categorized into following groups (a) disease diagnosis and detection, (b) disease risk prediction, (c) health monitoring, (d) healthcare related discoveries, and (e) epidemic outbreak prediction. The objective of the research is to help the researchers in this field to get a comprehensive overview of the machine learning applications in healthcare. Apart from revealing the potential of machine learning in healthcare, this paper will serve as a motivation to foster advanced research in the domain of machine intelligence-driven healthcare.

Keywords: Machine learning, big data, healthcare, health monitoring, disease diagnosis, disease risk prediction.

« Previous Next »
Graphical Abstract

[1]
Manyikaetal J. Big Data The Next Frontier for Innovation, Competition, and Productivity. New York, NY, USA: McKinsey Global Institute 2011.
[2]
Magoulas GD, Prentza A. Machine Learning in Medical Applications ACAI ’ 99. Springer 2001; pp. 300-7.
[http://dx.doi.org/10.1007/3-540-44673-7_19]
[3]
Shishavan OR, Zois OS, Soyata T. Machine intelligence in healthcare and medical cyber physical systems: A survey IEEE access 2018; 6: 46420-94.
[4]
Gautam P, Ansari MD, Sharma SK. Enhanced security for electronic health care information using obfuscation and RSA algorithm in cloud computing. Int J Inf Secur Priv 2019; 13: 59-69.
[http://dx.doi.org/10.4018/IJISP.2019010105]
[5]
Sethi K, Jaiswal V, Ansari MD. Machine learning based support system for students to select stream. Recent Pat Comput Sci 2019; 13(3): 12.
[6]
Tsai CW, Lai CF, Chiang MC, Yang LT. Data mining for internet of things: A survey. IEEE Comm Surv Tutor 2014; 16: 77-97.
[http://dx.doi.org/10.1109/SURV.2013.103013.00206]
[7]
Gandhi R. Introduction to machine learning algorithms: Linear regression Available at: https://towardsdatascience.com/introduction-to-machine-learning-algorithms-linear-regression-14c4e325882a
[8]
Brownlee J. Classification and regression trees for machine learning
[9]
Available at: https://machinelearningmastery.com/classification-and-regression-trees-for-machine-learning/
[10]
Kotsiantis SB. Supervised machine learning: A review of classification techniques. Informatica 2007; 31: 249-68.
[11]
Mahdavinejad MS, Rezvan M, Barekatain M, Adibi P, Barnaghi P, Sheth AP. Machine learning for internet of things data analysis: A survey. Digital Commun Netw 2018; 4: 161-75.
[http://dx.doi.org/10.1016/j.dcan.2017.10.002]
[12]
Gupta P. Naïve bayes in machine learning Available at: https://towardsdatascience.com/naive-bayes-in-machine-learning-f49cc8f831b4
[13]
Brownlee JK. Nearest neighbors for machine learning Available at: https://machinelearningmastery.com/k-nearest-neighbors-for-machine-learning/
[14]
Ani1 K, Jain JM. Artificial neural networks: A tutorial. Comput IEEE 1996; 29: 31-44.
[15]
Hinton GE. Deep belief networks. Scholarpedia 2009.
[16]
A Beginner's Guide to Generative Adversarial Networks (GANs) Available at: https://skymind.ai/wiki/generative-adversarial-network-gan
[17]
Mohammadi M, Al-Fuqaha A, Sorour S, Guizani M. Deep learning for IoT big data and streaming analytics: A survey. IEEE Comm Surv Tutor 2018; 20: 2923-60.
[http://dx.doi.org/10.1109/COMST.2018.2844341]
[18]
Esmael B, Arnaout A, Fruhwirth RK, Thonhauser G. Improving time series classification using hidden markov models. 2012 12th International Conference on Hybrid Intelligent Systems (HIS). Pune, India 2012.
[http://dx.doi.org/10.1109/HIS.2012.6421385]
[19]
Trevino A. Introduction to K-means clustering Available at: https://www.datascience.com/blog/k-means-clustering
[20]
Association learning Available at: https://deepai.org/machine-learning-glossary-and-terms/association-learning
[21]
Hastie T, Friedman J, Tibshirani R. Unsupervised learning the elements of statistical learning springer series in statistics. New York: Springer 2001.
[22]
Fournier-Viger P, Chun-wei JL, Bay V, Tin TC, Ji Z, Hoai BL. A survey of item set mining. WIREs Data Mining and Knowledge Discovery 2017; 7: 1-18.
[23]
Dimensionality reduction algorithms: Strengths and weaknesses. Available at: https://elitedatascience.com/dimensionality-reduction
[24]
Vasan KK, Surendiran B. Dimensionality reduction using principal component analysis for network intrusion detection. Perspect Sci 2016; 8: 510-2.
[25]
Lopes M. Is LDA a dimensionality reduction technique or a classifier algorithm? Available at: https://towardsdatascience.com/is-lda-a-dimensionality-reduction-technique-or-a-classifier-algorithm-eeed4de9953a
[26]
Ayushman SS. Reducing dimensionality of data using neural networks Available at: https://www.cse.iitk.ac.in/users/cs365/2015/_submissions/ayushmn/slides.pdf
[27]
Raymer ML, Punch WF, Goodman ED, Kuhn LA, Jain AK. Dimensionality reduction using genetic algorithms. IEEE Trans Evol Comput 2000; 4: 164-71.
[http://dx.doi.org/10.1109/4235.850656]
[28]
Ghumbre SU, Ghatol AA. Heart disease diagnosis using machine learning algorithm. Proceedings of the International Conference on Information Systems Design and Intelligent Applications 2012 (INDIA 2012) held in Visakhapatnam, India, January 2012.
[http://dx.doi.org/10.1007/978-3-642-27443-5_25]
[29]
Sontakke S, Lohokare J, Dani R. 2017 International Conference on Emerging Trends & Innovation in ICT (ICEI). Pune, India. 2017.
[http://dx.doi.org/10.1109/ETIICT.2017.7977023]
[30]
Bansal D, Chhikkara R, Kavita K, Poonal G. Comparative analysis of various machine learning algorithms for detecting dementia. Procedia Comput Sci 2018; 132: 1497-502.
[31]
Torti E, Florimbi G, Castelli F, et al. Parallel K-means clustering for brain cancer detection using hyperspectral images electronics. MDPI 2018; 2018: 7.
[32]
Abiyev RH, Mohammad KSM. Deep convolutional neural networks for chest diseases detection. J Healthc Eng Hindawi 2018.
[33]
Sisodia D, Sisodia DS. Prediction of diabetes using classification algorithms. Procedia Comput Sci 2018; 132: 1578-85.
[http://dx.doi.org/10.1016/j.procs.2018.05.122]
[34]
Abdelaziz A, Salama ASA, Riad M, Alia NM. A Machine Learning Model for Predicting of Chronic Kidney Disease Based Internet of Things and Cloud Computing in Smart Cities Security in Smart Cities: Models, Applications, and Challenges, Lecture Notes in Intelligent Transportation and Infrastructure. Springer 2019.
[35]
Yang JG, Kim JK, Kang UG, Lee YH. Coronary heart disease optimization system on adaptive-network based fuzzy inference system and linear discriminant analysis (ANFIS–LDA) Personal and Ubiquitous Computing. Springer 2013.
[36]
Layeghian JS, Sepehri MM, Aghajani H. Toward analyzing and synthesizing previous research in early prediction of cardiac arrest using machine learning based on a multi-layered integrative framework. J Biomed Inform 2018; 88: 70-89.
[37]
Akbuluta FP, Akan A. A smart wearable system for short-term cardiovascular risk assessment with emotional dynamics Measurement, Elsevier 2018; 128: 237-46.
[38]
Goyal M. Long short-term memory recurrent neural network for stroke prediction. International Conference on Machine Learning and Data Mining in Pattern Recognition 2018.
[39]
Moreira MWL, Rodrigues JJPC, Kumar N, Saleem K, Illin IV. Postpartum depression prediction through pregnancy data analysis for emotion-aware smart systems. Information Fusion, Elsevier 2019; 2019: 4723-31.
[http://dx.doi.org/10.1016/j.inffus.2018.07.001]
[40]
Asri H, Mousannif H, Al Moatassime H, Noel T. Using machine learning algorithms for breast cancer risk prediction and diagnosis. Procedia Comput Sci 2016; 83: 1064-9.
[http://dx.doi.org/10.1016/j.procs.2016.04.224]
[41]
Mathur R, Pathak V, Bandil D. Parkinson disease prediction using machine learning algorithm emerging trends in expert applications and security, advances in intelligent systems and computing. Springer 2019.
[42]
Moradi E, Antonietta P, Christian G, Heikki H, Jussi T. Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects. Neuroimage Elsevier 2015; 104: 398-412.
[43]
Manhua L, Danni C, Kundong W, Yaping W. Multi-modality cascaded convolutional neural networks for Alzheimer’s disease diagnosis. Neuroinformatics 2018; 16: 295-308.
[44]
Hao Y, Usama M, Yang JM, Shamim H, Ahmed G. Recurrent convolutional neural network based multimodal disease risk prediction. Future Gener Comput Syst 2019; 92: 76-83.
[45]
Ma F, Radha C, Jing Z, Quenzeng Y, Tong S, Jing G. Dipole: Diagnosis prediction in healthcare via attention-based bidirectional recurrent neural networks KDD’17. Canada: ACM 2017.
[46]
Chen M, Li W, Hao Y, Qian Y, Humar I. Edge cognitive computing based smart healthcare system. Future Gener Comput Syst 2018; 86: 403-11.
[http://dx.doi.org/10.1016/j.future.2018.03.054]
[47]
Pham M, Mengistu Y, Do H, Sheng W. Delivering home healthcare through a Cloud-based Smart Home Environment (CoSHE). Future Generation Computer Systems, Elsevier 2018; 81: 129-40.
[48]
Zia Uddin Md. A wearable sensor-based activity prediction system to facilitate edge computing in smart healthcare system. J Parallel Distrib Comput 2019; 123: 46-53.
[49]
Alemdar H, Can T, Ersoy C. Daily life behaviour monitoring for health assessment using machine learning: Bridging the gap between domains. Pers Ubiquitous Comput 2015; 19: 303-15.
[http://dx.doi.org/10.1007/s00779-014-0823-y]
[50]
Serpen G, Rakibul HK. Real-time detection of human falls in progress: Machine learning approach. Procedia Comput Sci 2018; 140: 238-47.
[51]
Ghulam M, Mansour A, Umar AS, Ahmed G, Alhamid MF. A facial-expression monitoring system for improved healthcare in smart cities. IEEE Access 2017; 5: 10871-81.
[52]
Srividya M, Mohanavalli S, Bhalaji N. Behavioral modeling for mental health using machine learning algorithms. J Med Syst 2018; 42(5): 88.
[53]
Abdellatif AA, Emam A, Carla FC, Amr M, Ali J, Rabab W. Edge based compression and classification for smart healthcare systems: Concept, implementation and evaluation. Expert systems with applications. Elsevie 2019; 117: 1-14.
[54]
Alhussein M, Muhammad G, Shamim HM, Syed UA. Cognitive IoT-cloud integration for smart healthcare: Case study for epileptic seizure detection and monitoring. Mob Netw Appl 2018; 23: 1624-35.
[55]
Sasaki S, Alexis JC, Hiroshi S, Chris B. Using genetic algorithms to optimise current and future health planning - The example of ambulance locations. Int J Health Geogr 2010; 2010: 9.
[56]
Kost R, Littenberg B, Chen ES. Exploring Generalized Association Rule Mining for Disease Co-. Occurrences. AMIA Annu Symp Proc 2012; 2012: 1284-93.
[57]
Rashid MA, Hoque MT, Sattar A. Association rules mining based clinical observations 2014. Available at: https://arxiv.org/ftp/arxiv/papers/1401/1401.2571.pdf
[58]
Wang B, Chen D, Shi B, et al. Comprehensive association rules mining of health examination data with an extended fp-growth method mobile network applications. Mob Netw Appl 2017; 22: 267-74.
[59]
Chen H, Engkvist O, Wang Y, Olivecrona M, Blaschke T. The rise of deep learning in drug discovery Drug Discovery Today. 2018; 23(6): 1241-50.
[60]
Yoon BJ. Hidden markov models and their applications in biological sequence analysis. Curr Genomics 2009; 10(6): 402-15.
[http://dx.doi.org/10.2174/138920209789177575]
[61]
Reps JM, Aickelin U, Ma J, Zhang Y. Refining adverse drug reactions using association rule mining for electronic healthcare data. 2014 IEEE International Conference on Data Mining Workshop. Shenzhen, China. 2014.
[http://dx.doi.org/10.1109/ICDMW. 2014.53]
[62]
Pandey MK, Subbiah K. Performance Analysis of Time Series Forecasting Using Machine Learning Algorithms for Prediction of Ebola Casualties. International Conference on Application of Computing and Communication Technologies 2018.
[63]
Najar AM, Irawan MI, Adzkiya D. Extreme learning machine method for dengue hemorrhagic fever outbreak risk level prediction. 2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE) Shah Alam, Malaysia.
[http://dx.doi.org/10.1109/ICSCEE.2018.8538409]
[64]
Li Z, Luo X, Wang B, Bertozzi AL, Xin J. A Study on GraphStructured Recurrent Neural Networks and Sparsification with Application to Epidemic Forecasting 2019. Available at: https://arxiv.org/abs/1902.05113
[65]
Babar Z, Mannan A, Kamiran F, Karim A. Understanding the Impact of Socio-Economic and Environmental Factors for Disease Outbreak in Developing Countries. IEEE 15th International Conference on Data Mining Workshops. Atlantic City, NJ, USA 2015.
[http://dx.doi.org/10.1109/ICDMW.2015.49]

Rights & Permissions Print Cite
Article Metrics
20
© 2024 Bentham Science Publishers | Privacy Policy