A Predictive model for liver disease progression based on logistic regression algorithm

Ahmad Shaker Abdalrada, Omar Hashim Yahya, Abdul Hadi M. Alaidi, Nasser Ali Hussein, Haider TH. Alrikabi, Tahsien Al-Quraishi Al-Quraishi

Abstract


Liver disease counts to be one of the most prevalent diseases in the worldwide. Therefore, this paper is aim to address the problem of predicting liver disease progression. As the existing predictive models focus on predicting the label of disease; the probability of developing the disease is still obscure. This paper, therefore, has proposed a model to predict the probability occurrence of liver diseases. The proposed predictive model used logistic regression abilities to predict the probability of liver disease occurrence. ILPD dataset was used to analyze the performance of the model. The predictive model has shown outstanding performance with a prediction accuracy rate of 72.4%, the sensitivity of 90.3%, the specificity of 78.3 %, Type I Error of 9.7 %, Type II Error of 21.7 %, and ROC of 0.758%. The model has furthermore confirmed the feasibility of the laboratory tests such as as (Age; Direct Bilirubin (DB), Alamine_Aminotransferase (SGPT), Total_Protiens (TP), Albumin (ALB)) to predict the disease progression. The predictive model will be helpful to patients and doctors to realize the progression of the disease and make a suitable timely intervention.

Keywords


Liver Disease Progression, Prediction, Logistic Regression, Machine Learning.

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References


T. C. ‐. Yip et al, "Laboratory parameter‐based machine learning model for excluding non‐alcoholic fatty liver disease (NAFLD) in the general population," Alimentary Pharmacology & Therapeutics, vol. 46, (4), pp. 447-456, 2017.

W. Li et al, "Multiparametric ultrasomics of significant liver fibrosis: A machine learning-based analysis," European Radiology, vol. 29, (3), pp. 1496-1506, 2019.

S. Rashid, A. Ahmed, I. Al Barazanchi, A. Mhana, and H. Rasheed, “Lung cancer classification using data mining and supervised learning algorithms on multi-dimensional data set,” Period. Eng. Nat. Sci., vol. 7, no. 2, pp. 438–447, 2019.

S Sindhuja, D., and R. Jemina Priyadarsini. "A survey on classification techniques in data mining for analyzing liver disease disorder." International Journal of Computer Science and Mobile Computing 5, no. 5 (2016): 483-488.

S. Rashid, A. Ahmed, I. Al Barazanchi, and Z. A. Jaaz, “Clustering algorithms subjected to K-mean and gaussian mixture model on multidimensional data set,” Period. Eng. Nat. Sci., vol. 7, no. 2, pp. 448–457, 2019.

N. Venkateswaran, A. Shekhar, and S. Changder, “Using machine learning for intelligent shard sizing on the cloud,” Period. Eng. Nat. Sci., vol. 7, no. 1, pp. 109–124, 2019.

C. Fernández Carrillo et al, "Treatment of hepatitis C virus infection in patients with cirrhosis and predictive value of model for end‐stage liver disease: Analysis of data from the Hepa‐C registry," Hepatology, vol. 65, (6), pp. 1810-1822, 2017.

S. Rashid, A. Ahmed, I. Al Barazanchi, A. Mhana, and H. Rasheed, “Lung cancer classification using data mining and supervised learning algorithms on multi-dimensional data set,” Period. Eng. Nat. Sci., vol. 7, no. 2, pp. 438–447, 2019.

B. Lykiardopoulos et al, "Development of Serum Marker Models to Increase Diagnostic Accuracy of Advanced Fibrosis in Nonalcoholic Fatty Liver Disease: The New LINKI Algorithm Compared with Established Algorithms," PloS One, vol. 11, (12), pp. e0167776-e0167776, 2016.

S. Liu et al, "Discriminating between adaptive and carcinogenic liver hypertrophy in rat studies using logistic ridge regression analysis of toxicogenomic data: The mode of action and predictive models," Toxicology and Applied Pharmacology, vol. 318, pp. 79-87, 2017.

H. R. Bdulshaheed, Z. T. Yaseen, and I. I. Al-barazanchi, “New approach for Big Data Analysis using Clustering Algorithms in Information,” Jour Adv Res. Dyn. Control Syst., vol. 2, no. 4, pp. 1194–1197, 2019.

H. Ma et al, "Application of Machine Learning Techniques for Clinical Predictive Modeling: A Cross-Sectional Study on Nonalcoholic Fatty Liver Disease in China," BioMed Research International, vol. 2018, pp. 4304376-9, 2018.

H. Hagström et al, "Fibrosis stage but not NASH predicts mortality and time to development of severe liver disease in biopsy-proven NAFLD," Journal of Hepatology, vol. 67, (6), pp. 1265, 2017.

M. Pons et al, "Basal values and changes of liver stiffness predict the risk of disease progression in compensated advanced chronic liver disease," Digestive and Liver Disease, vol. 48, (10), pp. 1214-1219, 2016.

Y. Xie et al, "Evaluation of a logistic regression model for predicting liver necroinflammation in hepatitis B e antigen‐negative chronic hepatitis B patients with normal and minimally increased alanine aminotransferase levels," Journal of Viral Hepatitis, vol. 26, (S1), pp. 42-49, 2019.

K. R. Bisaso et al, "A comparative study of logistic regression based machine learning techniques for prediction of early virological suppression in antiretroviral initiating HIV patients," BMC Medical Informatics and Decision Making, vol. 18, (1), pp. 77-77, 2018.

M. A. Konerman et al, "Assessing risk of fibrosis progression and liver-related clinical outcomes among patients with both early stage and advanced chronic hepatitis C," PloS One, vol. 12, (11), pp. e0187344-e0187344, 2017.

S. Petta et al, "Metabolic syndrome and severity of fibrosis in nonalcoholic fatty liver disease: An age‐dependent risk profiling study," Liver International, vol. 37, (9), pp. 1389-1396, 2017.

S. Saokaew et al, "Clinical risk scoring for predicting non‐alcoholic fatty liver disease in metabolic syndrome patients (NAFLD‐MS score)," Liver International, vol. 37, (10), pp. 1535-1543, 2017.

I. Al Barazanchi, S. A. Hamid, R. A. Abdulrahman, and H. Rasheed, “Automated telemedicine and diagnosis system ( ATDS ) in diagnosing ailments and prescribing drugs,” Period. Eng. Nat. Sci., vol. 7, no. 2, pp. 888–894, 2019.

T. Hayashi et al, "Noninvasive Assessment of Advanced Fibrosis Based on Hepatic Volume in Patients with Nonalcoholic Fatty Liver Disease," Gut and Liver, vol. 11, (5), pp. 674, 2017.

M. A. Konerman et al, "Machine learning models to predict disease progression among veterans with hepatitis C virus," PloS One, vol. 14, (1), pp. e0208141-e0208141, 2019.




DOI: http://dx.doi.org/10.21533/pen.v7i3.667

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Copyright (c) 2019 Ahmad Shaker Abdalrada, Omar Hashim Yahya, Abdul Hadi M. Alaidi, Nasser Ali Hussein, Haider TH. Alrikabi, Tahsien Al-Quraishi Al-Quraishi

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

ISSN: 2303-4521

Digital Object Identifier DOI: 10.21533/pen

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License