Abstract
Nonalcoholic Fatty Liver Disease (NAFLD) is the most common cause of chronic liver disease around the world. Remaining silent in the early stages makes its evaluation a challenge. Liver biopsy is still the gold standard method used to classify NAFLD stages but has important sample error issues and subjectivity in the interpretation. This research is an effort to overcome liver biopsy to a possible extent by forming a non-invasive clinical spectrum. This paper proposed an intelligent scheme using the forward algorithm, Viterbi algorithm, and Baum-welch algorithm for examining the disease, and a new clinical spectrum is introduced that incorporates most likely attributes associated with NAFLD stages. The experimental results verify that our method is efficient in distinguishing the credibility of an attribute being associated with a specific stage in case it is linked with more than one stage. Moreover, the proposed scheme can successfully estimate the likelihood of stage progression and supports medical knowledge more proficiently and realistically.
Highlights
Intelligent method based frameworks have played a vital role in medicine
To discover the most likely attributes associated with Nonalcoholic Fatty Liver Disease (NAFLD) stages given the health examination data
The approaches based on the hidden Markov model are especially known for describing the events or applications that are unpredictable due to the influence of random variables
Summary
Intelligent method based frameworks have played a vital role in medicine. Due to increasing vagueness and complexities in the datasets, deriving intelligible information becomes a significant challenge for clinicians. This challenge could lead to an imprecise assessment of the disease, which would further guide inaccurate treatments to patients. To avoid these uncertainties up to a feasible extent, medical professionals refer to the intelligent decision-making systems for a second thought on the interpretation of multifaceted datasets. Like for other health complications, intelligent methods have
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