How to Cite?
Adekola Olubukola Daniel, Ekanem Edikan Uwem, Omidiran Daniel Tolulope, Owoade Samuel Jesupelumi, "Prediction And Diagnosis of Liver Disease In Human Using Machine Learning," International Journal of Computer Trends and Technology, vol. 68, no. 8, pp. 44-52, 2020. Crossref, https://doi.org/10.14445/22312803/IJCTT-V68I8P107
Disease diagnosis is the most vital task in medicine and this mostly depends on doctor’s intuition based on experiences in the past. Unfortunately, the difficulties in recognizing correct symptoms results in a misdiagnosis. To avoid such medical misdiagnosis, this study utilized dataset to intelligently detect liver disease in humans. This study aimed to implement an effective data mining method and algorithm to predict and diagnose the occurrence of liver diseases in human in order to eliminate the use of manual methods of analysis relating to liver diseases. The study embodies case studies, systematic literature reviews and surveys. Important requirements were also identified in related papers. The relevant documents obtained were qualitatively analyzed for convergence and relevant details were extracted using inductive approach. Subsequently, a liver disease diagnosis system (LDDS) was developed to tackle the problem of early detection of the disease in humans. LDDS is a web application created to ease the prediction of the occurrence of liver disease in humans.
Misdiagnosis; Dataset; Machine-Learning algorithms; Data mining; Liver Disease Diagnosis System
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