Computer Science & IT Research Journal, Volume 3, Issue 1, January 2022 Jamila, Wajiga, Malgwi, & Maidabara, P. 36-51 Page 36 A DIAGNOSTIC MODEL FOR THE PREDICTION OF LIVER CIRRHOSIS USING MACHINE LEARNING TECHNIQUES Ganty, Jamila 1 , Gregory Msksha Wajiga 2 , Yusuf Musa Malgwi 3 & Abba Hamman Maidabara 4 1 No:3 Jiddari Polo near Bus Stop, Maiduguri, Borno State 2,3 Department of Computer Science, Modibbo Adama University, Yola, P.M.B. 2076 Yola, Adamawa State, Nigeria. 4 No: 45, Old G.R.A, Polo Ground Maidugu Street, Maiduguri, Borno State ______________________________________________________________________________ *Corresponding Author: Ganty, Jamila Corresponding Author Email: jamilaganty@gmail.com Article Received: 29-12-21 Accepted: 10-01-22 Published: 25-01-22 Licensing Details: Author retains the right of this article. The article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licences/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the Journal open access page ______________________________________________________________________________ ABSTRACT Liver cirrhosis is the most common type of chronic liver disease in the globe. The ability to forecast the onset of liver cirrhosis sickness is critical for successful treatment and the prevention of catastrophic health implications. As a result, the researchers created a prediction model using machine learning techniques. This study was based on a dataset from the Federal Medical Centre, Yola, which included 583 patient instances and 11 attributes. The proposed model for the prediction of liver cirrhosis sickness employed Nave Bayes, Classification and Regression Tree (CART), and Support Vector Machine (SVM) with 10-fold cross-validation. Accuracy, precision, recall, and F1 Score were used to evaluate the model's performance. Among all the strategies used in this study, the Support Vector Machine (SVM) technique produces the best results, with accuracy of 73%, precision of 73%, recall of 100%, and F1 Score of 84%. Based on medical data from FMC, Yola, this study shows that machine learning methods, specifically the Support Vector Machine, provide a more accurate prediction for liver cirrhosis sickness. This approach can be used to help doctors make better clinical decisions. ____________________________________________________________________________ OPEN ACCESS Computer Science & IT Research Journal P-ISSN: 2709-0043, E-ISSN: 2709-0051 Volume 3, Issue 1, P.36-51, January 2022 DOI: 10.51594/csitrj.v3i1.296 Fair East Publishers Journal Homepage: www.fepbl.com/index.php/csitrj