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Liver disease counts are one of the most prevalent diseases all over the world and they are becoming very common these days and can be dangerous. Liver diseases are increasing all over the world due to different factors such as excess alcohol consumption, drinking contaminated water, eating contaminated food, and exposure to polluted air. The liver is involved in many functions related to the human body and if not functioned properly can affect the other parts too. Predication of the disease at an earlier stage can help reduce the risk of severity. This paper implemented oversampling dataset, feature selecting attributes, and performance analysis for the improvement of the accuracy of classification of liver patients in 3 phases. In the first phase, the z-score normalization algorithm has been implemented to the original liver patient data-sets that has been collected from the UCI repository and then works on oversampling the balanced dataset. In the second phase, feature selection of attributes is more important by using RFE feature selection. In the third phase, classification algorithms are applied to the data-set. Finally, evaluation has been performed based upon the values of accuracy. Thus, outputs shown from proposed classification implementations indicate that ANN algorithm performs better than AdaBoost algorithm with the help of feature selection with a 92.77% accuracy


Machine learning Classification Feature selection RFE ANN AdaBoost Liver

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How to Cite
ahmed sami jaddoa, S. J. Saba, and E. A.Abd Al-Kareem, “Liver Disease Prediction Model Based on Oversampling Dataset with RFE Feature Selection using ANN and AdaBoost algorithms”, bit-cs, vol. 4, no. 2, pp. 85-93, Jul. 2023.


  1. H. Hartatik, M. B. Tamam, and A. Setyanto, “Prediction for Diagnosing Liver Disease in Patients using KNN and Naïve Bayes Algorithms,” 2020 2nd Int. Conf. Cybern. Intell. Syst. ICORIS 2020, pp. 1–5, 2020, doi: 10.1109/ICORIS50180.2020.9320797.
  2. M. A. Kuzhippallil, C. Joseph, and A. Kannan, “Comparative Analysis of Machine Learning Techniques for Indian Liver Disease Patients,” 2020 6th Int. Conf. Adv. Comput. Commun. Syst. ICACCS 2020, pp. 778–782, 2020, doi: 10.1109/ICACCS48705.2020.9074368.
  3. M. A. Khadija and N. A. Setiawan, “Detecting Liver Disease Diagnosis by Combining SMOTE, Information Gain Attribute Evaluation, and Ranker,” ITSMART J. Teknol. dan Inf., vol. 9, no. 1, pp. 13–17, 2020.
  4. A. S. Jaddoa, Z. Tariq, and M. Al-ta, “COMPARISON OF DATA MINING ALGORITHMS FOR DIAGNOSIS OF DIABETES MELLITUS,” vol. 10, no. 2, pp. 1–8, 2021.
  5. R. Ahmed, S. Jaddoa, P. Ziyad, and T. Mustafa, “Diagnosis of Diabetes Mellitus using Hybrid Techniques for Feature Selection and Classification,” pp. 1650–1663, 2021.
  6. S. Jain, R. Sharma, and R. Rajkamal, “EasyChair Preprint Classification of Liver Diseases Using Intelligent Techniques Classification of Liver Diseases Using Intelligent Techniques,” 2021.
  7. G. Jamila, G. M. Wajiga, Y. M. Malgwi, and A. H. Maidabara, “A Diagnostic Model for the Pediction of Liver Cirrhosis using Machine Learning Teachniques,” Comput. Sci. IT Res. J., vol. 3, no. 1, pp. 36–51, 2022, doi: 10.51594/csitrj.v3i1.296.
  8. G. S. Harshpreet Kaur, “The Diagnosis of Chronic Liver Disease using Machine Learning Techniques,” Inf. Technol. Ind., vol. 9, no. 2, pp. 554–564, 2021, doi: 10.17762/itii.v9i2.382.
  9. M. Ghosh et al., “A comparative analysis of machine learning algorithms to predict liver disease,” Intell. Autom. Soft Comput., vol. 30, no. 3, pp. 917–928, 2021, doi: 10.32604/iasc.2021.017989.
  10. N. Nahar, F. Ara, M. A. I. Neloy, V. Barua, M. S. Hossain, and K. Andersson, “A Comparative Analysis of the Ensemble Method for Liver Disease Prediction,” ICIET 2019 - 2nd Int. Conf. Innov. Eng. Technol., pp. 23–24, 2019, doi: 10.1109/ICIET48527.2019.9290507.
  11. S. Afrin et al., “Supervised machine learning based liver disease prediction approach with LASSO feature selection,” Bull. Electr. Eng. Informatics, vol. 10, no. 6, pp. 3369–3376, 2021, doi: 10.11591/eei.v10i6.3242.
  12. N. Tanwar and K. F. Rahman, “Machine learning in liver disease diagnosis: Current progress and future opportunities,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1022, no. 1, 2021, doi: 10.1088/1757-899X/1022/1/012029.
  13. R. C. Poonia et al., “Intelligent Diagnostic Prediction and Classification Models for Detection of Kidney Disease,” Healthc., vol. 10, no. 2, 2022, doi: 10.3390/healthcare10020371.
  14. S. Kefelegn, “Prediction and Analysis of Liver Disorder Diseases by using Data Mining Technique: Survey,” vol. 118, no. 9, pp. 765–770, 2017, [Online]. Available:
  15. S. Gupta, G. Karanth, N. Pentapati, and V. R. B. Prasad, “A Web Based Framework for Liver Disease Diagnosis using Combined Machine Learning Models,” Proc. - Int. Conf. Smart Electron. Commun. ICOSEC 2020, no. Icosec, pp. 421–428, 2020, doi: 10.1109/ICOSEC49089.2020.9215454.
  16. [16] A. Khatavkar, P. Potpose, and P. Pandey, “Smart Health Prediction System,” vol. 5, no. 02, pp. 1550–1552, 2017.
  17. B. K. Mengiste, H. K. Tripathy, and J. K. Rout, “Analysis and Prediction of Cardiovascular Disease Using Machine Learning Techniques,” Lect. Notes Electr. Eng., vol. 708, no. 2, pp. 133–141, 2021, doi: 10.1007/978-981-15-8685-9_13.