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Journal of Diabetes & Metabolic Disorders
Published in: Journal of Diabetes & Metabolic Disorders 2/2021

01-12-2021 | Commentary

A survey on data mining techniques used in medicine

Authors: Saba Maleki Birjandi, Seyed Hossein Khasteh

Published in: Journal of Diabetes & Metabolic Disorders | Issue 2/2021

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Abstract

Data mining is the process of analyzing a massive amount of data to identify meaningful patterns and detect relations, which can lead to future trend prediction and appropriate decision making. Data mining applications are significant in marketing, banking, medicine, etc. In this paper, we present an overview of data mining applications in medicine to provide a clear view of the challenges and previous works in this area for researchers. Data mining techniques such as Decision Tree, Random Forest, K-means Clustering, Support Vector Machine, Logistic Regression, Neural Network, Naive Bayes, and association rule mining are used for diagnosing, prognosis, classifying, constructing predictive models, and analyzing risk factors of various diseases. The main objective of the paper is to analyze and compare different data mining techniques used in the medical applications. We present a summary of the results and provide comparison analysis of the data mining methods employed by the reviewed articles.
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Literature
1.
Fayyad U, Piatetsky-Shapiro G, Smyth P. Knowledge Discovery and Data Mining: Towards a Unifying Framework. Proceeding KDD'96 Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. 1996:82–8.
2.
M. Durairaj VR. Data Mining Applications In Healthcare Sector: A Study. Int J Sci Technol Res. 2013;2(10).
3.
Song C-W, Jung H, Chung K. Development of a medical big-data mining process using topic modeling. Cluster Computing. 2017.
4.
Gonzalez GH, Tahsin T, Goodale BC, Greene AC, Greene CS. Recent advances and emerging applications in text and data mining for biomedical discovery. Brief Bioinform. 2016;17(1):33–42.PubMedCrossRef
5.
Salazar J, Espinoza C, Mindiola A, Bermudez V. Data mining and endocrine diseases: a new way to classify? Arch Med Res. 2018;49(3):213–5.PubMedCrossRef
6.
YoussefAgha AH, Lohrmann DK, Jayawardene WP. Use of data mining to reveal body mass index (BMI): patterns among Pennsylvania schoolchildren, pre-k to grade 12. J Sch Health. 2013;83(2):85–92.PubMedCrossRef
7.
Hosseini M, Ataei N, Aghamohammadi A, Yousefifard M, Taslimi S, Ataei F. The relation of body mass index and blood pressure in Iranian children and adolescents aged 7–18 years old. Iran J Public Health. 2010;39(4):126–34.PubMedPubMedCentral
8.
Hand D. Statistics and data mining: intersecting disciplines. ACM SIGKDD Explorations Newsl. 1999;1(1):16–9.CrossRef
9.
Makridakis S, Spiliotis E, Assimakopoulos V. Statistical and Machine Learning forecasting methods: Concerns and ways forward. PLoS One. 2018;13(3):e0194889.PubMedPubMedCentralCrossRef
10.
Kantardzic M. Data Mining: Concepts, Models, Methods, and Algorithms. second ed: Wiley-IEEE Press; 2011.
11.
Han J, Kamber M, Pei J. Data Mining: Concepts and Techniques. third ed: Morgan Kaufmann; 2011.
12.
Mitchell T. Machine Learning. first ed: McGraw-Hill Education; 1997.
13.
Worachartcheewan A, Schaduangrat N, Prachayasittikul V, Nantasenamat C. Data mining for the identification of metabolic syndrome status. EXCLI J. 2018;17:72–88.PubMedPubMedCentral
14.
Bishop C. Pattern Recognition and Machine Learning. first ed: Springer-Verlag New York; 2006. 738 p.
15.
Shaikhina T, Lowe D, Daga S, Briggs D, Higgins R, Khovanova N. Decision tree and random forest models for outcome prediction in antibody incompatible kidney transplantation. Biomed Signal Process Control. 2017.
16.
Meng XH, Huang YX, Rao DP, Zhang Q, Liu Q. Comparison of three data mining models for predicting diabetes or prediabetes by risk factors. Kaohsiung J Med Sci. 2013;29(2):93–9.PubMedCrossRef
17.
Tesfaye B, Atique S, Elias N, Dibaba L, Shabbir SA, Kebede M. Determinants and development of a web-based child mortality prediction model in resource-limited settings: a data mining approach. Comput Methods Programs Biomed. 2017;140:45–51.PubMedCrossRef
18.
Acharya UR, Ng WL, Rahmat K, Sudarshan VK, Koh JEW, Tan JH, et al. Data mining framework for breast lesion classification in shear wave ultrasound: a hybrid feature paradigm. Biomed Signal Process Control. 2017;33:400–10.CrossRef
19.
Mohammad Shafenoor Amin YKC, Varathan KD. Identification of significant features and data mining techniques in predicting heart disease. Telematics Inform. 2019;36:82–93.CrossRef
20.
Srabanti Maji SA. Decision tree algorithms for prediction of heart disease. Inform Commun Technol Compet Strat. 2019;40:447-54.
21.
Tayefi M, Tajfard M, Saffar S, Hanachi P, Amirabadizadeh AR, Esmaeily H, et al. hs-CRP is strongly associated with coronary heart disease (CHD): A data mining approach using decision tree algorithm. Comput Methods Programs Biomed. 2017;141:105–9.PubMedCrossRef
22.
Chen H-Y, Chuang C-H, Yang Y-J, Wu T-P. Exploring the risk factors of preterm birth using data mining. Expert Syst Appl. 2011;38(5):5384–7.CrossRef
23.
Khalilia, Chakraborty S, Popescu M. Predicting disease risks from highly imbalanced data using random forest. BMC Med Inform Decis Mak 2011;11.
24.
Ahamad M, Ahmed M, Uddin M. Clustering as Data Mining Technique in Risk factors Analysis of Diabetes, Hypertension and Obesity. Eur J Eng Res Sci. 2016;1.
25.
Han Wu SY, Zhangqin Huang, Jian He, Xiaoyi Wang (2018) Type 2 diabetes mellitus prediction model based on data mining. Inform Med Unlock.;10:100–7.
26.
Arslan AK, Colak C, Sarihan ME. Different medical data mining approaches based prediction of ischemic stroke. Comput Methods Programs Biomed. 2016;130:87–92.PubMedCrossRef
27.
Easton JF, Stephens CR, Angelova M. Risk factors and prediction of very short term versus short/intermediate term post-stroke mortality: a data mining approach. Comput Biol Med. 2014;54:199–210.PubMedCrossRef
28.
Heydari ST, Ayatollahi SM, Zare N. Comparison of artificial neural networks with logistic regression for detection of obesity. J Med Syst. 2012;36(4):2449–54.PubMedCrossRef
29.
Pochini A, Wu Y, Hu G. Data Mining for Lifestyle Risk Factors Associated with Overweight and Obesity among Adolescents. 2014 IIAI 3rd Int Conf Adv Appl Inform; 2014. p. 883–8.
30.
Charlton R, Gravenor M, Rees A, Knox G. Factors associated with low fitness in adolescents – A mixed methods study. BMC Public Health. 2014;14.
31.
Alizadehsani R, Habibi J, Hosseini MJ, Mashayekhi H, Boghrati R, Ghandeharioun A, et al. A data mining approach for diagnosis of coronary artery disease. Comput Methods Programs Biomed. 2013;111(1):52–61.PubMedCrossRef
32.
Adnan M, Husain W, Abdul Rashid N. Parameter Identification and Selection for Childhood Obesity Prediction Using Data Mining. 2nd International Conference on Management and Artificial Intelligence2012.
33.
Hossaina R, Mahmud S, Hossin M. PRMT: predicting risk factor of obesity among middle-aged people using data mining techniques. Inter Conf Comput Intell Data Sci. 2018:1068–76.
34.
Ilayaraja M, Meyyappan T. Mining medical data to identify frequent diseases using Apriori Algorithm. Proceedings of the 2013 Int Conf Pattern Recog Inform Mobile Eng 2013.
35.
Nahar J, Imam T, Tickle KS, Chen Y-PP. Association rule mining to detect factors which contribute to heart disease in males and females. Expert Syst Appl. 2013;40(4):1086–93.CrossRef
36.
Sharma S. Concept of association rule of data mining assists mitigating the increasing obesity. Int J Inf Ret Res. 2017;7(2):1–18.CrossRef
37.
Ramezankhani A, Pournik O, Shahrabi J, Azizi F, Hadaegh F. An application of association rule mining to extract risk pattern for type 2 diabetes using tehran lipid and glucose study database. Int J Endocrinol Metab. 2015;13(2):e25389.PubMedPubMedCentralCrossRef
38.
Salehnasab C, Jahandideh F, Ahmadzadeh M. Use association rules to study the relation between variables that affect high blood pressure. Acta HealthMedica. 2017;2(1)
39.
Ordonez C. Comparing Association Rules and Decision Trees for Disease Prediction. Proceeding HIKM '06 Proceedings of the international workshop on Healthcare information and knowledge management. 2006:17–24.
40.
41.
Chang C-D, Wang C-C, Jiang BC. Using data mining techniques for multi-diseases prediction modeling of hypertension and hyperlipidemia by common risk factors. Expert Syst Appl. 2011;38(5):5507–13.CrossRef
42.
Adnan MHM, Husain W, Rashid NAA. A Framework for Childhood Obesity Classifications and Predictions using NBtree. 2011 7th International Conference on Information Technology in Asia; 12–13 July 2011: IEEE; 2011.
43.
Wang HY, Chang SC, Lin WY, Chen CH, Chiang SH, Huang KY, et al. Machine Learning-Based Method for Obesity Risk Evaluation Using Single-Nucleotide Polymorphisms Derived from Next-Generation Sequencing. J Comput Biol. 2018;25(12):1347–60.PubMedCrossRef
44.
Vijayalakshmi N, Jenifer T. An analysis of risk factors for diabetes using data mining approach. Int J Comput Sci Mob Comput. 2017;6(7):166–72.
45.
Lingren T, Thaker V, Brady C, Namjou B, Kennebeck S, Bickel J, et al. Developing an algorithm to detect early childhood obesity in Two Tertiary Pediatric Medical Centers. Appl Clin Inform. 2016;7(3):693–706.PubMedPubMedCentralCrossRef
46.
Haifeng Wang BZ, Yoon SW, Ko HS. A support vector machine-based ensemble algorithm for breast cancer diagnosis. Eur J Oper Res. 2018;267(2):687–99.CrossRef
47.
Vilar S, Friedman C, Hripcsak G. Detection of drug-drug interactions through data mining studies using clinical sources, scientific literature and social media. Brief Bioinform. 2018;19(5):863–77.PubMedCrossRef
48.
Razavian N, Blecker S, Schmidt AM, Smith-McLallen A, Nigam S, Sontag D. Population-level prediction of type 2 diabetes from claims data and analysis of risk factors. Big Data. 2015;3(4):277–87.PubMedCrossRef
49.
Dev DA, McBride BA, Fiese BH, Jones BL, Cho H, Behalf Of The Strong Kids Research T. Risk factors for overweight/obesity in preschool children: an ecological approach. Child Obes. 2013;9(5):399–408.PubMedPubMedCentralCrossRef
50.
DE Rumelhart HG, Williams RJ. Learning representations by back-propagating errors. Nature. 1986;323:533–6.CrossRef
51.
Riccardo Miotto FW, Wang S, Jiang X, Dudley JT. Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform. 2018;19(6):1236–46.PubMedCrossRef
52.
Edward Choi AS, Stewart Walter F, Sun J. Using recurrent neural network models for early detection of heart failure onset. J Am Med Inform Assoc. 2017;24(2):361–70.PubMedCrossRef
53.
Adam Yala CL, Schuster Tal, Portnoi Tally, Barzilay Regina. A deep learning mammography-based model for improved breast cancer risk prediction. Radiology. 2019;292(1):60–6.PubMedCrossRef
54.
Diego Ardila APK, Bharadwaj S, Choi B. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med. 2019;25:954–61.PubMedCrossRef
55.
56.
Lakhani PSB. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology. 2017;284(2):574–82.PubMedCrossRef
57.
Gerhard-Paul Diller AK, Babu-Narayan SV. Machine learning algorithms estimating prognosis and guiding therapy in adult congenital heart disease: data froma single tertiary centre including 10 019 patients. Eur Heart J. 2019;40(13):1069–77.PubMedPubMedCentralCrossRef
58.
Ordonez C, Omiecinski E, de Braal L, Santana CA, Ezquerra N, Taboada JA, et al. Mining constrained association rules to predict heart disease. Proceedings 2001 IEEE Int Conf Data Min; 2001. p. 433–40.
59.
Ordonez C. Association rule discovery with the train and test approach for heart disease prediction. IEEE Trans Inf Technol Biomed. 2006;10.
60.
Bellazzi R, Zupan B. Predictive data mining in clinical medicine: current issues and guidelines. Int J Med Inform. 2008;77(2):81–97.PubMedCrossRef
61.
Jothi N, Rashid NAA, Husain W. Data mining in healthcare – a review. Procedia Computer Science. 2015;72:306–13.CrossRef
62.
RaminGhorbani RG. Predictive data mining approaches in medical diagnosis: a review of some diseases prediction. Int J Data Net Sci. 2019;3:47–70.
63.
Marinov i, Mosa M, Yoo l. Data-mining technologies for diabetes: a systematic review. J Diabetes Sci Technol. 2011;5(6(
64.
Kharya S. Using data mining techniques for diagnosis and prognosis of cancer disease. Int J Comput Sci, Eng Inf Technol. 2012;2(2):55–66.CrossRef
65.
Kaur B, Singh W. Review on Heart Disease Prediction System using Data Mining Techniques. Int J Recent Innov Trends Comput Commun. 2014;2(10(
66.
Muhammad Noman Sohail RJ, Muhammad Musa Uba. A Comprehensive Looks at Data Mining Techniques Contributing to Medical Data Growth: A Survey of Researcher Reviews. Recent Dev Intell Comput, Commun Devices. 2019;752:21–6.
Metadata
Title
A survey on data mining techniques used in medicine
Authors
Saba Maleki Birjandi
Seyed Hossein Khasteh
Publication date
01-12-2021
Publisher
Springer International Publishing
Published in
Journal of Diabetes & Metabolic Disorders / Issue 2/2021
Electronic ISSN: 2251-6581
DOI
https://doi.org/10.1007/s40200-021-00884-2

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WHO estimates that half of all patients worldwide are non-adherent to their prescribed medication. The consequences of poor adherence can be catastrophic, on both the individual and population level.

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