An intelligent hybrid approach for hepatitis disease diagnosis: Combining enhanced k‐means clustering and improved ensemble learning

Abstract

In real world, the automatic detection of liver disease is a challenging problem among medical practitioners. The intent of this work is to propose an intelligent hybrid approach for the diagnosis of hepatitis disease. The diagnosis is performed with the combination of k-means clustering and improved ensemble-driven learning. To avoid clinical experience and to reduce the evaluation time, ensemble learning is deployed, which constructs a set of hypotheses by using multiple learners to solve a liver disease problem. The performance analysis of the proposed integrated hybrid system is compared in terms of accuracy, true positive rate, precision, f-measure, kappa statistic, mean absolute error, and root mean squared error. Simulation results showed that the enhanced k-means clustering and improved ensemble learning with enhanced adaptive boosting, bagged decision tree, and J48 decision tree-based intelligent hybrid approach achieved better prediction outcomes than other existing individual and integrated methods.

Hybrid intelligence in hospitals: towards a research agenda for collaboration

“…4) Decisions (subjectiveobjective): Diagnosing and treating different diseases is strongly subjective, depending on individual experience and differing based on clinicians' emotions and mental states (Chang and Hsu 2009;Singh et al 2020). This applies to experienced professionals and actors with different levels of expertise, e.g., young assistant doctors with limited clinical educations (Laurenzi et al 2017;Singh et al 2020). Even though clinicians try to derive objective decisions, they always act with subjective influence and own judgments.…”

“…Objective and equal evaluation of patient data Non-prejudices decisions (e.g., objective conclusion based on medical facts) Independent decisions regardless of time or mental state (Chang and Hsu 2009;Gnewuch et al 2017;Laurenzi et al 2017;Seeber et al 2019;Singh et al 2020) Welcoming the interviewee and providing general information about the research and brief introduction to the topic. a) Can you imagine how CAs can be applied to improve collaboration and team-building in hospitals?…”

Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction

“…This variety regularly leads to the diagnosis of disease becoming a side issue for healthcare experts. In addition, the clinical interpretation of medical information is a cognitively challenging task [4]. This not only applies to experienced professionals but also to actors with different or little expertise such as young assistant doctors [4,5].…”

“…In addition, the clinical interpretation of medical information is a cognitively challenging task [4]. This not only applies to experienced professionals but also to actors with different or little expertise such as young assistant doctors [4,5]. Medical specialists' available time is usually limited [2,3] and diseases might evolve and patient dynamics change over time [6,7] making diagnostics a highly complex process [8,9].…”

Special issue on intelligent biomedical data analysis and processing

“…In this paper (Singh et al, 2020) presents an intelligent hybrid approach to diagnosing hepatitis disease. The diagnosis is performed with the combination of k‐means clustering and improved ensemble‐driven learning.…”