{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:06:08Z","timestamp":1777705568917,"version":"3.51.4"},"reference-count":8,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2022,1,25]]},"abstract":"<jats:p>It is the need of today\u2019s world, to deliver with quality health care services to meet the health needs of target populations. The healthcare system includes procedures of prevention and screening of all types of diseases, their treatment and diagnostics, recent research and development. These procedures must be maintained at a desired level of excellence, which comes under quality management. Quality management in healthcare incorporates with making of various quality policies, quality planning and assurance, quality control and quality improvement. Quality improvement (QI) is the scheme used for betterment of the services delivered to the patients, such as diagnosis and treatment. If these schemes are recent and advanced technology based, services provided would be cost effective, accurate, less time consuming and hassle-free for both healthcare provider as well as patients. In this study we are applying artificial intelligent and machine learning techniques to enhance the diagnosis accuracy of the liver fibrosis which is caused by hepatitis C virus (HCV). Generally, the SLBs (serial liver biopsies) are utilized to diagnose the liver fibrosis levels (LFLs), which is the gold standard method in this domain. However, SLB has various impediment and not appropriate to the patients which leads to higher prognosis cost with invasive way. So, there is a big research gap in the medical field to find out the alternative non-invasive approach\/method for SLB. The proposed data-driven intelligent model for identification of liver fibrosis using hybrid approach is designed and implemented to overcome the SLBs problems with higher diagnostic accuracy. The empirical mode decomposition (EMD) approach is used to extract the IMFs (intrinsic mode functions), which are used as input features to the ANN-J48 algorithm based intelligent classifiers. The proposed approach shows the evidence for utilization in a non-invasive way to diagnose the LFLs without high level clinical expert skills.<\/jats:p>","DOI":"10.3233\/jifs-189779","type":"journal-article","created":{"date-parts":[[2021,6,26]],"date-time":"2021-06-26T05:19:05Z","timestamp":1624684745000},"page":"1155-1169","source":"Crossref","is-referenced-by-count":9,"title":["Data driven intelligent model for quality management in healthcare"],"prefix":"10.1177","volume":"42","author":[{"given":"Nuzhat","family":"Fatema","sequence":"first","affiliation":[{"name":"Faculty of Business and Management, Universiti Sultan Zainal Abidin (UniSZA), Malaysia"},{"name":"Intelligent Prognostic Private Limited, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hasmat","family":"Malik","sequence":"additional","affiliation":[{"name":"BEARS, University Town, NUS Campus, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wakeel","family":"Ahmad","sequence":"additional","affiliation":[{"name":"College of Applied Industrial Technology, Electrical Engineering Technology Department, Jazan University, Jazan, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"6","key":"10.3233\/JIFS-189779_ref6","doi-asserted-by":"publisher","first-page":"889","DOI":"10.1049\/iet-rpg.2015.0382","article-title":"Artificial Neural Network and Empirical Mode Decomposition Based Imbalance Fault Diagnosis of Wind Turbine Using TurbSim, FAST and Simulink,","volume":"11","author":"Malik","year":"2017","journal-title":"IET Renewable Power Generation"},{"issue":"4","key":"10.3233\/JIFS-189779_ref7","doi-asserted-by":"publisher","first-page":"3043","DOI":"10.3233\/JIFS-169247","article-title":"EMD and ANN based Intelligent Fault Diagnosis Model for Transmission Line","volume":"32","author":"Malik","year":"2017","journal-title":"Journal of Intelligent & Fuzzy Systems"},{"key":"10.3233\/JIFS-189779_ref9","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/BF00116251","article-title":"Induction of decision trees","volume":"1","author":"Quinlan","year":"1986","journal-title":"Machine Learning"},{"key":"10.3233\/JIFS-189779_ref10","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1023\/A:1022631118932","article-title":"Very Simple Classification Rules Perform Well on Most Commonly Used Datasets","volume":"11","author":"Holte","year":"1993","journal-title":"Machine Learning"},{"key":"10.3233\/JIFS-189779_ref11","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1023\/A:1008280620621","article-title":"Overcoming the Myopia of Inductive Learning Algorithms with RELIEFF,","volume":"7","author":"Kononenko","year":"1997","journal-title":"Applied Intelligence"},{"key":"10.3233\/JIFS-189779_ref13","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1007\/978-981-15-1532-3_18","article-title":"Data-Driven Intelligent Model for Sale Price Prediction and Monitoring of a Building, In Soft Computing in Condition Monitoring and Diagnostics of Electrical and Mechanical Systems","volume":"1096","author":"Fatema","year":"2020","journal-title":"Advances in Intelligent Systems and Computing"},{"issue":"5","key":"10.3233\/JIFS-189779_ref14","doi-asserted-by":"publisher","first-page":"5021","DOI":"10.3233\/JIFS-169786","article-title":"k-NN and ANN based deterministic and probabilistic wind speed forecasting intelligent approach","volume":"35","author":"Abdul","year":"2018","journal-title":"Journal of Intelligent & Fuzzy Systems"},{"issue":"16","key":"10.3233\/JIFS-189779_ref15","doi-asserted-by":"crossref","first-page":"1849","DOI":"10.1080\/15325008.2014.956952","article-title":"Selection of Most Relevant Input Parameters Using Waikato Environment for Knowledge Analysis for Gene Expression Programming Based Power Transformer Fault Diagnosis","volume":"42","author":"Mishra","year":"2014","journal-title":"International Journal of Electric Power Components and Systems"}],"container-title":["Journal of Intelligent &amp; 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