Forecasting Diabetes Correlated Non-alcoholic Fatty Liver Disease by Exploiting Naïve Bayes Tree


  • Shiva Shankar Reddy Biju Patnaik University of Technology image/svg+xml
  • Nilambar Sethi GIET University image/svg+xml
  • R. Rajender LENDI Engineering College, Vizianagaram, India
  • Gadiraju Mahesh SRKR Engineering College, Bhimavaram, India



Non-alcoholic fatty liver, diabetes mellitus, ensemble techniques, naive bayes, C4.5 decision tree, bagging, random forest, ada-boost, NB tree, accuracy, detection rate, NPV, FNR and FPR, diabetes mellitus (DM)


INTRODUCTION: In recent years, non-alcoholic fatty liver disease (NAFLD) has been identified as the most vulnerable chronic disease. Fat is accumulated in the liver cells of persons with NAFLD. Diabetes is the most common ailment among people of all ages, so it is critical to recognize and prevent its adverse effects.

OBJECTIVES: A relevant dataset with appropriate features was selected. Ensemble algorithms were applied for the prediction task, and finally, the method with the best performance was extracted.

METHODS: In addition to Ensemble approaches namely bagging, Random forest and Ada-boost, individual classifiers Naive Bayes (NB) and C4.5 Decision tree were considered. These ML techniques were compared with the proposed NB tree algorithm, a combination of C4.5 and Naive Bayes.

RESULTS: The following evaluation parameters were computed for each analyzed algorithm: accuracy, detection rate, negative predictive value (NPV), false negative rate (FNR), and false positive rate (FPR). The algorithms are then compared based on these metrics to determine the best algorithm. The NB tree was obtained to be the best method with 97.55% accuracy, 0.4853 detection rate, 0.9615 NPV, 0.0388 FNR, and 0.0099 FPR.

CONCLUSION: The NB tree outperformed individual Naive bayes and C4.5 classifiers, and the other techniques studied. The developed algorithm could be applied in NAFLD-related research.


Non alcoholic fatty liver disease [online]. Mayo Clinic; [cited 2020 October 21]. Available from: https://

Davis CP, Shiel WC. Liver Blood Tests [online]. Medicine Net; [cited 2020 June 22]. Available from:

Bhatt HB, Smith RJ. Fatty liver disease in diabetes mellitus. Hepatobiliary Surg Nutr. 2015; 4(2):101-108.

Singh A. Risk of non-alcoholic fatty liver disease in patients with type-1 diabetes [online]. ATLAS of Science; [cited 2019 Feb 18]. Available from:

Symptoms & Causes of NAFLD & NASH What are the symptoms of NAFLD and NASH? [online]. NIH: National Institute of Diabetes and Digestive and Kidney Diseases; [cited 2016 November]. Available from:

Lala V, Goyal A, Bansal P, Minter, DA. Liver Function Tests [online]. StatPearls [online]; [cited 2020 July 4]. Available from:

Reddy SS, Sethi N, Rajender R. Evaluation of Deep Belief Network to Predict Hospital Readmission of Diabetic Patients. In: Proceedings of 2020 Second International Conference on Inventive Research in Computing Applications ; 2020; Coimbatore, India. IEEE; 2020. p. 5-9.

Sarwar MA, Kamal N, Hamid W, Shah MA. Prediction of Diabetes Using Machine Learning Algorithms in Healthcare. In: Proceedings of 24th International Conference on Automation and Computing (ICAC); 2018; Newcastle upon Tyne,United Kingdom. IEEE; 2018. p. 1-6.

Reddy SS, Rajender R, Sethi N. A data mining scheme for detection and classification of diabetes mellitus using voting expert strategy. International J. of Knowledge-Based and Intelligent Engineering Systems. 2019; 23(2):103-8.

Vijayan VV, Anjali C. Prediction and diagnosis of diabetes mellitus - A machine learning approach. In: Proceedings of Recent Advances in Intelligent Computational Systems ; 2015; Trivandrum, India. IEEE; 2015. P. 122-127.

Reddy SS, Sethi N, Rajender R. Mining of multiple ailments correlated to diabetes mellitus. Evolutionary Intelligence. 2020; 1-8.

Kulkarni A, Shinde S, Kadam D. Automated Prediction of Non Alcoholic Fatty Liver Disease using Machine Learning Algorithms. International Research J. of Engineering and Technology (IRJET). 2020; 7(9):488-491.

Reddy SS, Sethi N, Rajender R. A Review of Data Mining Schemes for Prediction of Diabetes Mellitus and Correlated Ailments. In: Proceedings of 5th International Conference On Computing, Communication, Control And Automation (ICCUBEA); 2019; Pune, India. IEEE; 2019. p. 1-5.

Deo R, Panigrahi S. Prediction of Hepatic Steatosis (Fatty Liver) using Machine Learning. In: Proceedings of the 2019 3rd International Conference on Computational Biology and Bioinformatics; 2019; ACM. p. 8-12.

Chen M, Zhao X. (2018). Fatty Liver Disease Prediction Based on Multi-Layer Random Forest Model. In: Proceedings of the 2nd Int. Conference on Computer Science and Artificial Intelligence; 2018; ACM. p. 364-368.

Perveen S, Shahbaz M, Keshavjee K, Guergachi A. A Systematic Machine Learning Based Approach for the Diagnosis of Non-Alcoholic Fatty Liver Disease Risk and Progression. Scientific Reports. 2018; 8(2112).

Wu CC, Yeh WC, Hsu WD, Islam M, Nguyen PA, Poly TN, Wang YC, Yang HC, Li YC. Prediction of fatty liver disease using machine learning algorithms. Computer Methods and Programs in Biomedicine. 2019; 170:23-29.

Wang H, Zhang Y. Detection of motor imagery EEG signals employing Naïve Bayes based learning process. Measurement. 2016 May 1;86:148-58.

Islam MM, Wu CC, Poly TN, Yang HC, Li YJ. Applications of Machine Learning in Fatty Live Disease Prediction. Studies in Health Technology and Informatics. 2018; 247:166-170.

Kapoor S, Verma R, Panda SN. Detecting Kidney Disease using Naive Bayes and Decision Tree in Machine Learning. International J. of Innovative Technology and Exploring Engineering (IJITEE). 2019; 9(1):498-501.

Bashir S, Qamar U, Khan FH, Javed MY. An Efficient Rule-based Classification of Diabetes Using ID3, C4.5 & CART Ensembles. In: Proceedings of 12th International Conf. on Frontiers of Information Technology; FIT; 2014.

Perveen S, Shahbaz M, Guergachi A, Keshavjee K. Performance Analysis of Data Mining Classification Techniques to Predict Diabetes. In: Proceedings of Procedia Computer Science; 2016; 82:115 – 121

VijiyaKumar K, Lavanya B, Nirmala I, Caroline SS. Random Forest Algorithm for the Prediction of Diabetes. In: Proceedings of 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN); 2019; Pondicherry, India. IEEE; 2019. p. 1-5.

Chen P, Pan C. Diabetes classification model based on boosting algorithms. BMC bioinformatics. 2018; 19:109.

Devi TS, Sundaram KM. A Comparative Analysis of Meta and Tree Classification Algorithms using Weka. International Research J. of Engineering and Technology (IRJET). 2016; 3(11):77-83.

Mahmood DY, Hussein MA. Analyzing NB, DT and NB Tree Intrusion Detection Algorithms. Journal of Zankoy Sulaimani- Part A (JZS-A). 2014; 16(1):87-94.




How to Cite

Reddy SS, Sethi N, Rajender R, Mahesh G. Forecasting Diabetes Correlated Non-alcoholic Fatty Liver Disease by Exploiting Naïve Bayes Tree. EAI Endorsed Scal Inf Syst [Internet]. 2022 Apr. 29 [cited 2022 Dec. 3];10(1):e2. Available from: