NIT6130 - Literature Review: Machine Learning Applications in Medicine

This assignment is a literature review focusing on the applications of machine learning in medicine. The research process is documented, starting with a broad scan using Google Scholar, various libraries, and IEEE papers to identify relevant studies. The review details the research problem, background, and a comprehensive literature review, culminating in a summary and discussion. The document includes a research journal, filing system, and bibliography to showcase the systematic approach used to gather and filter information. The final outline and introduction to the literature review chapter are also presented, providing a complete overview of the research.

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Introduction to Research
Assignment 2

Executive Summary
The aim of this assignment is to conduct a literature research on the given topic “Machine
Learning in Medicine”. This paper is divided into five sections: first two sections Broad Scan
and Focused Scan shows how the research was carried out to collect the required information
and to make a filter what all is useful.
The third section is completely about the topic. All the information collected regarding the topic
has been presented in this section. Fourth section depicts the layout of the third section and last
section provides an introduction to complete research paper.
After a thorough research on Google scholar, various libraries and IEEE papers two final papers
were chosen to conduct a deep research and to collect the literature review material for the third
section of the document.
1

Table of Contents
1. Broad Scan...................................................................................................................................3
1.1 Research Journal....................................................................................................................3
1.2 Filing System.........................................................................................................................4
1.3 Bibliography for literature selected.......................................................................................6
2. Focused Review...........................................................................................................................6
2.1 Updated Filing System..........................................................................................................6
2.2 Updated Bibliography............................................................................................................6
3. Machine Learning in Medical Diagnosis.....................................................................................7
3.1 Research Problem..................................................................................................................7
3.2 Background Details...............................................................................................................7
3.3 Literature Review..................................................................................................................8
3.4 Summary and Discussion......................................................................................................9
4. Final Outline of Literature Review Chapter..............................................................................10
5. Introduction to Literature Review Chapter................................................................................10
References......................................................................................................................................12
2

1. Broad Scan
I started my research on Google Scholar to collect the most relevant studies regarding the topic
“Machine Learning in Medicine”. I then advanced my research to various libraries such as VU
library and also looked out for various IEEE conference papers online. Based on my research I
found approximately 30 to 40 papers which were in some manner related to the topic of the
study.
1.1 Research Journal
Date Task Action Comment
29/08/2018 A research for
relevant papers for the
topic of study
Entered appropriate
keywords in Google
explorer
More than 10000
papers popped up and
some of them which
looked relevant were
selected
30/08/2018 More research on the
topic
Made a search though
libraries and IEEE
conference papers
Papers which were
related to topic in a
good way were
selected and saved
2/09/2018 Literature Reading Out of the chosen
papers, selected 20 of
them and gave them a
quick reading
Had an understanding
of what machine
learning in medicine
is about
3/09/2018 Literature Reading From the documents
collected on 30th
August, selected
around 30 more
papers and read the
title, looked out for
source, date it was
published on.
Got more relevant
papers for the
assignment
3

4/09/2018 Filtration of selected
Journals
Based on the quick
reading and type of
document , date it was
published on, title of
the document
Got more relevant
papers for the
assignment
7/09/2018 Selection of two most
relevant papers
Selected five most
relevant papers of
focused review and
read them thoroughly
Decided on 2 papers
to continue with
writing assignment
8/09/2018 Filled out the filing
journals for both
broad scan section
and focused review
section
Made a table of all the
papers collected and
their source
No remarks
9/09/2018 Started with the
section 3
Based on first paper
out of 2 selected
papers. Prepared and
written the section 3
All information from
the paper was
paraphrased in my
own words
10/09/2018 More on section 3 Based on second
paper out of 2 selected
papers. Prepared and
written the section 3
All information from
the paper was
paraphrased in my
own words
11/09/2018 Completed section 4
and 5
Based on section 3
made an outline and
typed it
Completed the
Assignment before
deadline
1.2 Filing System
Source Keywords Used Number Returned
Literature
Number collected
Literature
Search Engine Google Machine learning in 78965 10
4

medicine
Machine learning in
Pharma
Medical diagnosis
though Machine
Learning
9877
4563
4
3
Google Scholar Machine learning in
Pharma research
papers pdf
Breast cancer MRI
diagnosis
26745
8056
8
5
IEEE Benefits of Machine
learning in Healthcare
Need For Medical
Specialization In
Machine Learning
345
786
3
4
VU Library Role of machine
learning in medicinal
science
Machine learning in
medicine
56784
2320
2
1
5

1.3 Bibliography for literature selected
Azar, A.T. and Metwally, S.M. (2013). Decision tree classifiers for automated medical diagnosis,
Neural Comput. Appl., vol. 23, no. 7–8, pp. 2387–2403.
Barros, R. C. and Basgalupp, M. P. (2014). Evolutionary Design of Decision-Tree Algorithms
Tailored to Microarray Gene Expression Data Sets, IEEE Trans. Evol. Comput., vol. 18, no. 6,
pp. 873–892.
Breiman, L. (2006). Bagging predictors. Mach Learn;24:123–140.
Chang, C.L. and Chen, H. (2009). Applying decision tree and neural network to increase quality
of dermatologic diagnosis, IEEE Research Paper, vol. 36, no. 2, Part 2, pp. 4035–4041, Mar.
2009.
Çınar, M., Engin, M., Engin, Z. and Ziya, Y. (2009). Early prostate cancer diagnosis by using
artificial neural networks and support vector machines, Expert Syst. Appl., vol. 36, no. 3, Part 2,
pp. 6357–6361, Apr. 2009.
Corren, J. (2011). Lebrikizumab treatment in adults with asthma. N Engl J Med; 365:1088–1098.
Cruz R., Mesa, A., Carrillo, C. and Barrientos, M. (2009). Discovering inter observer variability
in the cyto-diagnosis of breast cancer using decision trees and Bayesian networks, Appl. Soft
Comput., vol. 9, no. 4, pp. 1331–1342.
Das, A. and Bhattacharya, M. (2009). A Study on Prognosis of Brain Tumors Using Fuzzy Logic
and Genetic Algorithm Based Techniques, in International Joint Conference on Bioinformatics,
Systems Biology and Intelligent Computing, IJCBS ‟09, pp. 348–351.
Deepa, S. N. and Devi, B.A. (2011). A Survey on Artificial Intelligence Approaches for Medical
Image Classification, Indian J. Sci. Technol., vol. 4, no. 11, pp. 1583–1595.
Delen, G., Walker, D. and Kadam, A. (2015). Predicting Breast Cancer Survivability: A
Comparison of Three Data Mining Methods, Artif Intell Med, vol. 34, no. 2, pp. 113–127.
Esfandiari, N., Babavalian, M.R., Moghadam, R. and Tabar, V.K. (2014). Machine learning in
medicine, Expert Syst. Appl., vol. 41, no. 9, pp. 4434–4463.
6

Freitas, D. C., Wieser, A. and Apweiler, R. (2010). On the importance of comprehensible
classification models for protein function prediction, IEEEACM Trans. Comput. Biol.
Bioinforma. IEEE ACM, vol. 7, no. 1, pp. 172–182.
Guyon, I. and Elisseeff, A. (2013). An introduction to variable and feature selection. J Mach
Learn Res; 3:1157–1182.
Han, M., Kamber, J. and Pei, J. (2011). Data Mining: Concepts and Techniques, 3rd Revised ed.
Burlington, MA: Morgan Kaufmann Publishers In.
Hassanien, A.E. and Kim, T. (2012). Breast cancer MRI diagnosis approach using support
vector machine and pulse coupled neural networks, J. Appl. Log., vol. 10, no. 4, pp. 277–284.
Hastie, T., Tibshirani, R. and Friedman, J. (2009). The Elements of Statistical Learning,
Springer Science & Business Media, New York, NY
Ishwaran, H. and Kogalur, U.B. (2008). Lauer MS. Random survival forests. Ann Appl Stat;
2:841–860.
Jagger, C., Andersen, K., Breteler, M.M., Copeland, J.R., Helmer, C. and Baldereschi, M.
(2016). Prognosis with dementia in Europe: A collaborative study of population-based cohorts.
Neurologic Diseases in the Elderly Research Group. Neurology. 54(11 Suppl 5):S16–20. PMID:
10854356.
Kannel, W.B., Doyle, J.T., McNamara, P.M., Quickenton, P. and Gordon, T. (2015). Precursors
of sudden coronary death. Factors related to the incidence of sudden death. Circulation, 51:606–
613.
Komlagan, M., Pan, X., Domenger, J.P., Collins, D.L. and Coupe, P. (2014). Anatomically
Constrained Weak Classifier Fusion for Early Detection of Alzheimer’s Disease. In: Wu G,
Zhang D, Zhou L, editors. Machine Learning in Medical Imaging, p. 141–8.
Kourou, K., Exarchos, T.P., Exarchos, K.P., Karamouzis, M.V. and Fotiadis, D.I. (2015).
Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J;
https://doi.org/10.1016/j. csbj.2014.11.005 PMID: 25750696.
7

Lahsasna, A., Ainon, L.R. Zainuddin, L. and Bulgiba, A. (2012). Design of a fuzzy-based
decision support system for coronary heart disease diagnosis, J. Med. Syst., vol. 36, no. 5, pp.
3293–3306.
Liu, F. and Chen, H. (2013). Inter-modality relationship constrained multi-modality multi-task
feature selection for Alzheimer’s Disease and mild cognitive impairment identification.
NeuroImage; 84:466–75. https://doi.org/10.1016/j.neuroimage. PMID: 24045077 51.
Louridas, P. and Ebert, C. (2016). Machine Learning. IEEE Softw. Sep; 33(5):110–5.
Nguyen, C. Wang, Y. and Nguyen, H.N. (2013). Random forest classifier combined with feature
selection for breast cancer diagnosis and prognostic, J. Biomed. Sci. Eng., vol. 06, no. 05, pp.
551–560.
Ota, K., Oishi, N., Ito, K. and Fukuyama, H. (2013). A comparison of three brain atlases for
MCI prediction. J Neurosci Methods. 221:139–50.
https://doi.org/10.1016/j.jneumeth.2013.10.003 PMID: 24140118
Othman, D. and Yau, T. M. (2007). Comparison of Different Classification Techniques Using
WEKA for Breast Cancer, in 3rd Kuala Lumpur International Conference on Biomedical
Engineering, pp. 520–523.
Ozcift, A. (2011). Enhanced Cancer Recognition System Based on Random Forests Feature
Elimination Algorithm, J. Med. Syst., vol. 36, no. 4, pp. 2577–2585.
Padilla, P., López, M., Górriz, J.M., and Ramírez, K. (2012). Disease Neuroimaging Initiative,
“NMF-SVM based CAD tool applied to functional brain images for the diagnosis of Alzheimer‟s
disease,” IEEE Trans. Med. Imaging, vol. 31, no. 2, pp. 207–216.
Schaefer, G. and Nakashima, T. (2010). Data Mining of Gene Expression Data by Fuzzy and
Hybrid Fuzzy Methods, IEEE Trans. Inf. Technol. Biomed., vol. 14, no. 1, pp. 23–29.
Shilaskar, S. and Ghatol, A. (2013). Feature selection for medical diagnosis : Evaluation for
cardiovascular diseases, Expert Syst. Appl., vol. 40, no. 10, pp. 4146–4153.
8

Suh, G.H. and Shah, A. (2011). A review of the epidemiological transition in dementia—cross-
national comparisons of the indices related to Alzheimer’s disease and vascular dementia. Acta
Psychiatrica Scandinavica; 104(1):4–11. PMID: 11437743.
Suk, H.I. and Shen, D. (2013). Deep learning-based feature representation for AD/MCI
classification. Med Image Comput Comput-Assist Interv MICCAI Int Conf Med Image Comput
Comput-Assist Interv. 16(Pt 2):583–90.
Temurtas, H., Yumusak, N. and Temurtas, F. (2015). A comparative study on diabetes disease
diagnosis using neural networks, Expert Syst. Appl., vol. 36, no. 4, pp. 8610–8615.
Vapnik, V.N. (2009). An overview of statistical learning theory. IEEE Trans Neural Netw;
10:988–999
Vatankhah, V., Asadpour, J. and Fazel-Rezai, R. (2013). Perceptual pain classification using
ANFIS adapted RBF kernel support vector machine for therapeutic usage, Appl. Soft Comput.,
vol. 13, no. 5, pp. 2537– 2546.
Woodruff, P.G., Modrek, B., Choy, D.F. and Jia, G. (2009). T-helper type 2-driven inflammation
defines major subphenotypes of asthma. Am J Respir Crit Care Med;180:388–395.
Zhang, S., Wang, G. and Dong, Z. (2013). An MR brain images classifier system via particle
swarm optimization and kernel support vector machine, Sci. World J., vol. 13.
2. Focused Review
2.1 Updated Filing System
Source Keywords Used Number Returned
Literature
Number collected
Literature
Search Engine Google Machine learning in 78965 7
9

medicine
Medical diagnosis
though Machine
Learning
4563
3
Google Scholar Breast cancer MRI
diagnosis
26745 5
IEEE Need For Medical
Specialization In
Machine Learning
345 3
VU Library Role of machine
learning in medicinal
science
56784 2
2.2 Updated Bibliography
Azar, A.T. and Metwally, S.M. (2013). Decision tree classifiers for automated medical diagnosis,
Neural Comput. Appl., vol. 23, no. 7–8, pp. 2387–2403.
Barros, R. C. and Basgalupp, M. P. (2014). Evolutionary Design of Decision-Tree Algorithms
Tailored to Microarray Gene Expression Data Sets, IEEE Trans. Evol. Comput., vol. 18, no. 6,
pp. 873–892.
Breiman, L. (2006). Bagging predictors. Mach Learn;24:123–140.
Chang, C.L. and Chen, H. (2009). Applying decision tree and neural network to increase quality
of dermatologic diagnosis, IEEE Research Paper, vol. 36, no. 2, Part 2, pp. 4035–4041, Mar.
2009.
Das, A. and Bhattacharya, M. (2009). A Study on Prognosis of Brain Tumors Using Fuzzy Logic
and Genetic Algorithm Based Techniques, in International Joint Conference on Bioinformatics,
Systems Biology and Intelligent Computing, IJCBS ‟09, pp. 348–351.
10

Deepa, S. N. and Devi, B.A. (2011). A Survey on Artificial Intelligence Approaches for Medical
Image Classification, Indian J. Sci. Technol., vol. 4, no. 11, pp. 1583–1595.
Freitas, D. C., Wieser, A. and Apweiler, R. (2010). On the importance of comprehensible
classification models for protein function prediction, IEEEACM Trans. Comput. Biol.
Bioinforma. IEEE ACM, vol. 7, no. 1, pp. 172–182.
Guyon, I. and Elisseeff, A. (2013). An introduction to variable and feature selection. J Mach
Learn Res; 3:1157–1182.
Hassanien, A.E. and Kim, T. (2012). Breast cancer MRI diagnosis approach using support
vector machine and pulse coupled neural networks, J. Appl. Log., vol. 10, no. 4, pp. 277–284.
Hastie, T., Tibshirani, R. and Friedman, J. (2009). The Elements of Statistical Learning,
Springer Science & Business Media, New York, NY
Jagger, C., Andersen, K., Breteler, M.M., Copeland, J.R., Helmer, C. and Baldereschi, M.
(2016). Prognosis with dementia in Europe: A collaborative study of population-based cohorts.
Neurologic Diseases in the Elderly Research Group. Neurology. 54(11 Suppl 5):S16–20. PMID:
10854356.
Kourou, K., Exarchos, T.P., Exarchos, K.P., Karamouzis, M.V. and Fotiadis, D.I. (2015).
Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J;
https://doi.org/10.1016/j. csbj.2014.11.005 PMID: 25750696.
Lahsasna, A., Ainon, L.R. Zainuddin, L. and Bulgiba, A. (2012). Design of a fuzzy-based
decision support system for coronary heart disease diagnosis, J. Med. Syst., vol. 36, no. 5, pp.
3293–3306.
Louridas, P. and Ebert, C. (2016). Machine Learning. IEEE Softw. Sep; 33(5):110–5.
Nguyen, C. Wang, Y. and Nguyen, H.N. (2013). Random forest classifier combined with feature
selection for breast cancer diagnosis and prognostic, J. Biomed. Sci. Eng., vol. 06, no. 05, pp.
551–560.
11