Machine Learning Based Liver Cirrhosis Detection Using Different Algorithm : A Review

Priyal Jain; Prakash Saxena

doi:10.32628/IJSRST2411353

Authors

Priyal Jain Department of Electronics and Communication Engineering, Bansal Institute of Science and Technology, Bhopal, Maharashtra, India Author
Prakash Saxena Department of Electronics and Communication Engineering, Bansal Institute of Science and Technology, Bhopal, Maharashtra, India Author

DOI:

https://doi.org/10.32628/IJSRST2411353

Keywords:

Cirrhosis, Fatty Liver, Diagnosis, Chronic, Diseases, Procedure, Illness

Abstract

Cirrhosis of the liver, a chronic hepatic condition marked by fibrosis and impaired functionality, poses substantial clinical difficulties worldwide. This comprehensive research review delves deeply into the study of liver cirrhosis, including its etiology, diagnosis, various treatment modalities, and prognosis. Various etiological variables, including conditions such as viral hepatitis, non-alcoholic fatty liver disease, and persistent alcohol consumption, contribute to the expansion of this syndrome via distinct mechanisms. Utilizing advanced methods such as transient elastography is crucial for achieving a precise diagnosis, which depends on a combination of clinical judgment, imaging tests, and histological examination. Various therapeutic approaches may be used based on the harshness of the patients state, including liver transplantation, pharmacotherapy, endoscopic interventions, and modifications in lifestyle. The prediction of the illness is contingent upon the severity of the cirrhosis and the resulting complications. Severe cirrhosis is correlated with an increased likelihood of death, in addition to the development of hepatocellular cancer. Healthcare professionals must collaborate across several disciplines to provide optimal care to their patients.

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