Increase Quality Of Machine Learning In Medicine

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.

Date	Task	Action	Comment Save Time On Research and Writing Hire a Pro to Write You a 100% Plagiarism-Free Paper. Get My Paper
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
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

Source	Keywords Used	Number Returned Literature	Number collected Literature
Search Engine Google	Machine learning in medicine Machine learning in Pharma Medical diagnosis though Machine Learning	78965 9877 4563	10 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

Source	Keywords Used	Number Returned Literature	Number collected Literature
Search Engine Google	Machine learning in medicine Medical diagnosis though Machine Learning	78965 4563	7 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

The algorithms of machine learning can fundamentally help in taking care of the medicinal services issues by manufacturing classified frameworks which can help in assistance to diagnose and anticipate infectious diseases in their early stages. Conversely, the task of extracting information from the medicinal data can be challenging depending upon how the data has been organized. It could be high dimensional, unorganized and heterogeneous and might also possess outliers and noisy data.

To select the best approach in terms of comprehensibility and accuracy it is must to analyze each of the available machine learning techniques and validate the performance. In this literature review, the algorithms of machine learning that has been analyzed for the purpose of accurate medical diagnosis are swarm intelligence, evolutionary algorithms, random forest, support vector machine and decision tree.

Machine Learning is an art of learning and making predictions from the past data with the help of artificial intelligence tools. It achieves this with the help of algorithms or methods developed to make computer intelligent. It is most useful in those cases where the information domain is in scarcity. The greatest advantages of using machine learning are scalability, customizability, speed, automation and accuracy.

Various applications in medicine sector works on the principle of classification algorithms. It is a two level process. The first level is training phase where a classifier is build using some set of tuples and the next level is classification phase in which classification is performed on the model and its performance is evaluated based on the set of tuples defined in phase 1.

Decision Tree Algorithm

This algorithm is categorized as classification algorithm. A tree is constructed on the basis of divide and conquers strategy. Some set of attributes represents instances. The tree is comprised of leaves and nodes: where nodes are used to represent various attribute values and leaves to bind all the nodes in a class structure. Outcome is Boolean.

Support Vector Machine

This algorithm is also categorized as classification algorithm and uses statistical learning methodology. In Support Vector Machine, hyperplane: an optimal boundary is acquired autonomously on the probabilistic appropriation of preparing vectors in the set.  

Research Journal

Random Forests

Again an example of classification algorithm, Random Forests are best in cases of handling large amount of data with high accuracy. It is a blend of tree indicators where each tree relies upon the estimations of an arbitrary vector tested autonomously with a similar conveyance for every one of the trees in the woods.

Evolutionary Algorithm

This is a strategy to find optimal solutions in wide and complex set of databases. This algorithm is motivated by general evolution: a number of determined candidate solutions which are called chromosomes are developed with the help of operations like mutation and crossover.

Swarm Intelligence

This is a technology for the solution to real-world problems. In the collected database of population, behaviors of individuals are analyzed collectively who in turn interacts with each other inside their own environment within a control system decentralized.

To classify the medical data is a very complex process and then it is needed to be optimized. Multiple kinds of machine learning algorithms we learned about in the previous section are tested many researchers and based on the research different algorithms serves different purposes.

Medical Classification

Due to its simplicity of nature and interpretability, decision algorithm is considered to be the best option for the purpose of medical classification. It acts as a classifier for the diagnosis of brain tumor, liver cancer, dermatological diseases and breast cancer. Decision algorithm performances has been compared to many other classification algorithms on the basis of KNN, Bayesian Network, logistic regression, case based reasoning and ANN. In breast cancer diagnosis it has an accuracy rate of 99.5 percent. Regression tree and classification model was proposed by a researcher Luke which provided differences amongst non-malignant liver tissue and HCC. HCC is believed to be very dangerous because of its diagnosis at later stages. Decision tree algorithm helped to discover such hidden patterns and building classification model depending upon it.   

Identification of Disease and Generalization

Support vector algorithms provide an advanced generalization to the medical classification technique. For example: If decision tree algorithm helps in detecting a breast cancer then SVM helps to detect the type of breast cancer. SVM uses Wisconsin breast cancer diagnosis techniques to detect the cancer and achieves high accuracy. It is also used to successfully detect liver, diabetes and heart diseases which are generally genetic and fuzzy.

Medical Diagnosis

Four medical datasets: lung cancer, breast cancer, leukemia cancer and colon cancer was analyzed using random forest algorithm for the purpose of selection of optimal features to diagnose this dataset and to come up with the best strategically solution. Its accuracy was compared to 15 other trained algorithms and this has shown 99.87 percent accuracy.

Filing System

Optimization

The optimization of classifier algorithms are achieved with the help of pre-processing tools such as ACO and PSO which are part of Swarm Intelligence algorithms. It helps to increase the accuracy of classification algorithms such as decision tree, random forest etc. and makes sure that resources have been utilized to minimum.

Effective execution of machine learning strategies for the purpose of diagnosing medical diseases can support the association of technology in the health environment. Specifically, in underdeveloped countries and countries with a high population ration where doctor patient ratio may rise to 1:1800, machine learning methods for detecting diseases at an early stage could be very beneficial. Innovation can no chance supplant a doctor’s skill and expertise, however it can deal with generally clear yet time expending analytic errands and specialists can take up clinically high demand methodology.

The aim of this assignment is to conduct a literature research on the given topic “Machine Learning in Medicine”. Machine Learning is an art of learning and making predictions from the past data with the help of artificial intelligence tools. It achieves this with the help of algorithms or methods developed to make computer intelligent. It is most useful in those cases where the information domain is in scarcity. The greatest advantages of using machine learning are scalability, customizability, speed, automation and accuracy.

The algorithms of machine learning can fundamentally help in taking care of the medicinal services. To select the best approach in terms of comprehensibility and accuracy it is must to analyze each of the available machine learning techniques and validate the performance. In this literature review, the algorithms of machine learning that has been analyzed for the purpose of accurate medical diagnosis are swarm intelligence, evolutionary algorithms, random forest, support vector machine and decision tree.

References

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.

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.

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.

Focused Review

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.

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.

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.

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.

Updated Filing System

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.

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.

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.

Ç?nar, 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.

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.

Machine Learning in Medical Diagnosis

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.

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.

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.