Novel Hybridized Computational Paradigms Integrated with Five Stand-Alone Algorithms for Clinical Prediction of HCV Status among Patients: A Data-Driven Technique

Abstract

The emergence of health informatics opens new opportunities and doors for different disease diagnoses. The current work proposed the implementation of five different stand-alone techniques coupled with four different novel hybridized paradigms for the clinical prediction of hepatitis C status among patients, using both sociodemographic and clinical input variables. Both the visualized and quantitative performances of the stand-alone algorithms present the capability of the Gaussian process regression (GPR), Generalized neural network (GRNN), and Interactive linear regression (ILR) over the Support Vector Regression (SVR) and Adaptive neuro-fuzzy inference system (ANFIS) models. Hence, due to the lower performance of the stand-alone algorithms at a certain point, four different novel hybrid data intelligent algorithms were proposed, including: interactive linear regression-Gaussian process regression (ILR-GPR), interactive linear regression-generalized neural network (ILR-GRNN), interactive linear regression-Support Vector Regression (ILR-SVR), and interactive linear regression-adaptive neuro-fuzzy inference system (ILR-ANFIS), to boost the prediction accuracy of the stand-alone techniques in the clinical prediction of hepatitis C among patients. Based on the quantitative prediction skills presented by the novel hybridized paradigms, the proposed techniques were able to enhance the performance efficiency of the single paradigms up to 44% and 45% in the calibration and validation phases, respectively.

Validation of Instruments for the Improvement of Interprofessional Education through Educational Management: An Internet of Things (IoT)-Based Machine Learning Approach

“…Cognitive modelling is also considered a form of AI that aims to replicate human cognitive processes and understand how humans think, learn, and solve problems. This is focused on ensuring that AI systems align with human values and goals and do not behave unexpectedly or harmfully [27][28][29][30][31].…”

Chemometrics Feasibility for Modelling Chromatographic Behavior of Diazepam using Linear and Non-Linear Techniques: A Data Mining Based Approach

“…The second one involves the small scale or sometimes considered as the local structures, which mainly focused on modelling and detecting some anomalies as well as decide if there is high performance for fitness to decide whether there is chance for their occurrence (Bala et al, 2023;Rezaei-Darzi et al, 2014; A. G. . Data mining has been in existence for long, which is recently known as derogatory way, fishing through data and trawling Abba, Benaafi, Usman, Ozsahin, et al, 2023;Jibril, Zayyan, et al, 2023;Madaki et al, 2022;Pham et al, 2019;Rong et al, 2018;A. G. Usman, Ghali, et al, 2022;D.…”