Disease Predictive Models for Healthcare by using Data Mining Techniques: State of the Art

Disease Predictive Models for Healthcare by using Data Mining Techniques: State of the Art

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2020 by IJETT Journal
Volume-68 Issue-10
Year of Publication : 2020
Authors : Aman, Rajender Singh Chhillar
DOI :  10.14445/22315381/IJETT-V68I10P209

Citation 

MLA Style: Aman, Rajender Singh Chhillar  "Disease Predictive Models for Healthcare by using Data Mining Techniques: State of the Art" International Journal of Engineering Trends and Technology 68.10(2020):52-57. 

APA Style:Aman, Rajender Singh Chhillar. Disease Predictive Models for Healthcare by using Data Mining Techniques: State of the Art  International Journal of Engineering Trends and Technology, 68(10),52-57.

Abstract
Data mining in healthcare has a tremendous ability to explore the hidden pattern in medical datasets. These patterns can be helpful in both Disease diagnosis and prognosis. However, the raw medical data is complex, distributed, and large in size. Due to this, it becomes nearly impossible for the physician to manually process these data all alone. Analyzing this complex data requires lots of effort, time and money. It brings the need for automated Disease Predictive Models which will predict the disease with higher accuracy with lesser efforts. The usage of data mining in Indian healthcare sector has shown excellent potential for growth. This paper offers a summary of Data Mining Techniques, their Applications and current state of India in Healthcare sector in a systematic manner.

Reference

[1] “India’s spending on health sector has grown: Nadda - The Economic Times.” https://economictimes.indiatimes.com/industry/healthcare/biotech/healthcare/indias-spending-on-health-sector-has-grown-nadda/articleshow/65309487.cms?from=mdr (accessed Dec. 10, 2019).
[2] R. Kumar and R. Pal, “India achieves WHO recommended doctor population ratio: A call for paradigm shift in public health discourse!,” J. Fam. Med. Prim. care, vol. 7, no. 5, pp. 841–844, 2018, doi: 10.4103/jfmpc.jfmpc_218_18.
[3] “Big Data Analytics And Indian Healthcare - Express Healthcare.” https://www.expresshealthcare.in/features/big-data-analytics-and-indian-healthcare/162330/ (accessed Dec. 11, 2019).
[4] S. Bashir, Z. S. Khan, F. Hassan Khan, A. Anjum, and K. Bashir, “Improving Heart Disease Prediction Using Feature Selection Approaches,” Proc. 2019 16th Int. Bhurban Conf. Appl. Sci. Technol. IBCAST 2019, pp. 619–623, 2019, doi: 10.1109/IBCAST.2019.8667106.
[5] Arif-Ul-Islam and S. H. Ripon, “Rule Induction and Prediction of Chronic Kidney Disease Using Boosting Classifiers, Ant-Miner and J48 Decision Tree,” in 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), Feb. 2019, pp. 1–6, doi: 10.1109/ECACE.2019.8679388.
[6] S. H. Ebenuwa and M. H. D. S. Sharif, “Variance Ranking Attributes Selection Techniques for Binary Classification Problem in Imbalance Data,” IEEE Access, vol. 7, pp. 24649–24666, 2019, doi: 10.1109/ACCESS.2019.2899578.
[7] C. Zhou, Y. Jia, and M. Motani, “Optimizing Autoencoders for Learning Deep Representations from Health Data,” IEEE J. Biomed. Heal. Informatics, vol. 23, no. 1, pp. 103–111, 2019, doi: 10.1109/JBHI.2018.2856820.
[8] Q. Al-Tashi, H. Rais, and S. J. Abdulkadir, “Hybrid Swarm Intelligence Algorithms with Ensemble Machine Learning for Medical Diagnosis,” 2018 4th Int. Conf. Comput. Inf. Sci. Revolutionising Digit. Landsc. Sustain. Smart Soc. ICCOINS 2018 - Proc., pp. 1–6, 2018, doi: 10.1109/ICCOINS.2018.8510615.
[9] G. Chhabra, V. Vashisht, and J. Ranjan, “A classifier ensemble machine learning approach to improve efficiency for missing value imputation,” 2018 Int. Conf. Comput. Power Commun. Technol. GUCON 2018, no. April, pp. 23–27, 2019, doi: 10.1109/GUCON.2018.8674904.
[10] S. Rani, “Association Clustering and Time Series Based Data Mining in Continuous Data for Diabetes Prediction,” 2018 Second Int. Conf. Intell. Comput. Control Syst., no. Iciccs, pp. 1209–1214, 2018.
[11] E. Avci, S. Karakus, O. Ozmen, and D. Avci, “Performance comparison of some classifiers on Chronic Kidney Disease data,” 6th Int. Symp. Digit. Forensic Secur. ISDFS 2018 - Proceeding, vol. 2018-Janua, pp. 1–4, 2018, doi: 10.1109/ISDFS.2018.8355392.
[12] A. Mir and S. N. Dhage, “Diabetes Disease Prediction Using Machine Learning on Big Data of Healthcare,” Proc. - 2018 4th Int. Conf. Comput. Commun. Control Autom. ICCUBEA 2018, pp. 1–6, 2019, doi: 10.1109/ICCUBEA.2018.8697439.
[13] R. Singh, N. Rajpal, and R. Mehta, “Abnormality detection in ECG using hybrid feature extraction approach,” 2018 First Int. Conf. Secur. Cyber Comput. Commun., pp. 461–466, 2019, doi: 10.1109/icsccc.2018.8703349.
[14] V. Solanki, “Brain MRI Image Classification using Image Mining Algorithms,” 2018 Second Int. Conf. Comput. Methodol. Commun., no. Iccmc, pp. 516–519, 2018, doi: 10.1109/ICCMC.2018.8487690.
[15] K. Pahwa, A. C. S. Dangare, S. S. Apte, and I. Study, “Prediction of Heart Disease Using Hybrid Technique For Selecting Features,” pp. 500–504, 2017.
[16] W. Chen, S. Chen, H. Zhang, and T. Wu, “A hybrid prediction model for type 2 diabetes using K-means and decision tree,” Proc. IEEE Int. Conf. Softw. Eng. Serv. Sci. ICSESS, vol. 2017-Novem, no. 61272399, pp. 386–390, 2018, doi: 10.1109/ICSESS.2017.8342938.
[17] S. Rajeswari, M. S. Josephine, and V. Jeyabalaraja, “Dimension Reduction: A PSO-PCNN Optimization Approach for Attribute Selection in High-Dimensional Medical Database,” 2017 IEEE Int. Conf. Power, Control. Signals Instrum. Eng., pp. 2306–2309, 2017.
[18] “UCI Machine Learning Repository.” https://archive.ics.uci.edu/ml/index.php (accessed Dec. 10, 2019).
[19] “KEEL: A software tool to assess evolutionary algorithms for Data Mining problems (regression, classification, clustering, pattern mining and so on).” https://sci2s.ugr.es/keel/description.php (accessed Dec. 10, 2019).
[20] “Datasets | Kaggle.” https://www.kaggle.com/datasets (accessed Dec. 10, 2019).
[21] D. H. Wolpert and W. G. Macready, “No free lunch theorems for search,” Unknown, 1995, doi: 10.1145/1389095.1389254.
[22] “Healthcare Industry in India, Indian Healthcare Sector, Services.” https://www.ibef.org/industry/healthcare-india.aspx (accessed Dec. 11, 2019).
[23] “Health System for a New India: Building Blocks,” Niti Aayog, 2019. https://niti.gov.in/sites/default/files/2019-11/NitiAayogBook_compressed.pdf (accessed Dec. 11, 2019).
[24] N. Watts et al., “The 2018 report of the Lancet Countdown on health and climate change: shaping the health of nations for centuries to come,” The Lancet. 2018, doi: 10.1016/S0140-6736(18)32594-7.
[25] USF Health, “Data Mining In Healthcare | USF Health Online,” 2019. https://www.usfhealthonline.com/resources/key-concepts/data-mining-in-healthcare/ (accessed Dec. 12, 2019).

Keywords
Data Mining, Predictive Modeling, Healthcare, India.