Techniques for Data Mining Prediction in the Health Care Sector
DOI:
https://doi.org/10.55524/ijircst.2023.11.3.6Keywords:
Mining, Classification, Clustering, Machine LearningAbstract
Data mining is another term for knowledge discovery in databases (KDD). It's an interdisciplinary field that focuses on rooting meaningful knowledge from data in all sectors similar as health, education, and business. Currently, with the covid epidemic affecting everyone and rising coronavirus cases causing nursing home beds, oxygen, vaccines and individuals to be denied by hospitals, the health structure of the elderly is in the spotlight. There's a wealth of information accessible in the medical world about these diseases. Data booby-trapping concepts may be used to prize meaningful styles from this type of material in order to prognosticate unborn followings. This study emphasizes on several mining approaches that will be applied in the therapy assiduity to achieve the stylish results.
Downloads
References
Baek J W and Chung K 2020 , “Context deep neural network model for predicting depression risk using multiple regression “, IEEE Access 8 pp 18171-81
Khan F A, Zeb K A M, Derhab A, Bukhari S A C 2021, “Detection and Prediction of Diabetes using Data Mining A Comprehensive Review IEEE Access”
Delen D, Walker G, Kadam A 2005 “Predicting breast cancer survivability: a comparison of three data mining methods Artificial intelligence in medicine 34(2) pp 113- 27”
Luo L, Luo L, Zhang X, He X 2017, “Hospital daily outpatient visits forecasting using a combinatorial model based on ARIMA”,models BMC health services research 17(1) pp 1-13
B Taneja A 2013, “ Heart disease prediction system using data mining techniques”, Oriental Journal of Computer science and technology 6(4) pp 457-66
B Krishnaiah V, Narsimha G, Chandra N S 2016, “Heart disease prediction system using data mining techniques and intelligent fuzzy approach a review ,” International Journal of Computer Applications 136(2) pp 43-51
Mahmoud H, Abbas E, Fathy I 2018, “Data mining and ontology-based techniques in healthcare management”, International Journal of Intelligent Engineering Informatics 6(6) pp 509-26
Naveenkumar S, Kirubhakaran R, Jeeva G, Shobana M, Sangeetha K Smart, “Health Prediction Using Machine Learning”
Kumar H and Singh N 2017, “Review paper on Big Data in healthcare informatics”, International Research Journal of Engineering and Technology 4(2) pp 197-201
Er O, Yumusak N, Temurtas F 2010, “ Chest diseases diagnosis using artificial neural networks”, Expert Systems with Applications 37(12) pp 7648-55
Tang P H and Tseng M H 2009, “ Medical data mining using BGA and RGA for weighting of features in fuzzy k NN classification”, In 2009 International Conference on Machine Learning and Cybernetics IEEE 5 pp 3070-75
Balakrishnan S and Narayanaswamy R 2009 Feature selection using fcbf in type ii diabetes databases International Journal of the Computer the Internet and the
Management 17(1) pp 50-8
Chaurasia V and Pal S 2013, “ Early prediction of heart diseases using data mining techniques”, Caribbean Journal of Science and Technology pp 208-17
Bahrami B and Shirvani M H 2015 “Prediction and diagnosis of heart disease by data mining techniques”, Journal of Multidisciplinary Engineering Science and Technology (JMEST) 2(2) pp 164-8
Anu Sharma, M.K Sharma, Rakesh Kr. Dwivedi, “Exploratory data analysis and deception detection in news articles on social media using machine learning classifiers”, Ain Shams Engineering Journal, Volume 14, Issue 10, 2023, 102166, ISSN 2090-4479.
Anu Sharma et.al, “Literature Review and Challenges of Data Mining Techniques for Social Network Analysis,” Advances in Computational Sciences and Technology, 2017.
Downloads
Published
Issue
Section
How to Cite
