# A survey on artificial intelligence based techniques for diagnosis of hepatitis variants

### Abstract

Hepatitis is a dreaded disease that has taken the lives of so many people over the recent past years. The research survey shows that hepatitis viral disease has five major variants referred to as Hepatitis A, B, C, D, and E. Scholars over the years have tried to find an alternative diagnostic means for hepatitis disease using artificial intelligence (AI) techniques in order to save lives. This study extensively reviewed 37 papers on AI based techniques for diagnosing core hepatitis viral disease. Results showed that Hepatitis B (30%) and C (3%) were the only types of hepatitis the AI-based techniques were used to diagnose and properly classified out of the five major types, while (67%) of the paper reviewed diagnosed hepatitis disease based on the different AI based approach but were not classified into any of the five major types. Results from the study also revealed that 18 out of the 37 papers reviewed used hybrid approach, while the remaining 19 used single AI based approach. This shows no significance in terms of technique usage in modeling intelligence into application. This study reveals furthermore a serious gap in knowledge in terms of single hepatitis type prediction or diagnosis in all the papers considered, and recommends that the future road map should be in the aspect of integrating the major hepatitis variants into a single predictive model using effective intelligent machine learning techniques in order to reduce cost of diagnosis and quick treatment of patients.

### Downloads

### References

P. Nancy, V. Sudha, and R. Akiladevi, “Analysis of feature selection and classification algorithms on hepatitis data”, Int. J Advanced Res Comp. Eng. Technol., vol. 6, no. 1, pp. 19–23, 2017.

P. Dakshata and S. Seema, “Artificial intelligence based expert system for hepatitis B diagnosis,” Int. J Modeling Optimization, vol. 1, no. 4, pp. 362–366, 2011.

Y. A. Henok, “Adaptive learning expert system for diagnosis and management of viral hepatitis”, Int. J Artificial Intelli Applicat, vol. 10, no. 2, pp. 33–46, 2019; doi: 10.5121/ijaia.2019.10204.33

World Health Organization “Prevention, care and treatment of viral hepatitis in the African region: framework for action, 2016-2020,” Regional Office for Africa, 2017.

P. Korkmaz, N. Demirturk, A. Batırel, A. C. Yardimci, U. Cagir, S. A. Nemli, and F. Karakecili, “Noninvasive models to predict liver fibrosis in patients with chronic hepatitis B: a study from Turkey,” Hepatitis Monthly, vol. 17, no. 12, 2017.

R. K. Dhiman, National Guidelines for Diagnosis & Management of Viral Hepatitis, National Health Mission, India, 2018.

Q. K. Al-Shayea, “Artificial neural networks in medical diagnosis,” Int. J Computer Sci, vol. 8, no. 2, pp. 150-154, 2011.

S. A. Oke, “A literature review on artificial intelligence”, Int. J Management Sci, vol. 19, no. 4, pp. 535–570, 2008.

M. S. Okundamiya, Modern concepts in artificial intelligence, 2nd ed. Benin City: Stemic Publications, 2011.

M. S. Okundamiya, Modelling and optimization of a hybrid energy system for GSM base transceiver station sites in emerging cities, Ph.D. Thesis, University of Benin, Benin City, Nigeria, 2015.

M. O. Omisere, O. W Samuel, and E. J. Atajeromavwo, “A genetic-neuro-fuzzy inferential model for diagnosis of tuberculosis,” Appl Computing Informatics, 2015.

Y. Peng and X Zhang, “Integrative data mining in systems biology: from text to network mining,” Artificial Intelligence Med, vol. 41, no. 2, pp. 83–86, 2007.

S. E. Nnebe, N. A. O. Okoh, A. M. John-Otumu, and E. O. Oshoiribhor, “A neuro-fuzzy case based reasoning framework for detecting Lassa fever based on observed symptoms,” American J Artificial Intelligence, vol. 3, no. 1, pp. 9-16, 2019.

M. Nagarajasri, and M. Padmavathamma, “Threshold neuro fuzzy expert system for diagnosis of breast cancer,” International J Computer Applications, vol. 66, no. 8, pp. 6-10, 2013.

G. J. Klir and Y. Bo, Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers by Lotfi A. Zadeh, World Scientific Publishing Co., Inc., pp. 394–432, 1996.

E. H. Mamdani and A. Sedrak, “An experiment in linguistic synthesis with a fuzzy logic Controller,” Int. J Man-Machine Studies, vol. 7, no. 1, pp. 1-13, 1975.

A. Omotosho, A. E. Oluwatobi, and O. R. Oluwaseun, “A neuro-fuzzy system for the classification of cells as cancerous or non-cancerous,” Int. J Medical Res Health Sci, vol. 7, no. 5, pp. 155-166, 2018.

R. O. Osaseri, E. A. Onibere, and A. R. Usiobiafo, “Fuzzy Expert Model for Diagnosis of Lassa fever”, J Nigerian Association of Mathematical Physics, vol. 27, no. 1, pp. 533-540, 2014.

E. F. Aminu, A. A. Ajani, O. R. Isah, A. Ilyasu, A. O. Isah, and A. Z. Hussaini, “A diagnosis system for Lassa fever and related ailments using fuzzy logic”, J Science, Technol. Mathematics & Education, vol. 14, no. 2, pp. 18–30, 2018.

M. Hamad, “Lung cancer diagnosis by using fuzzy logic”, Int. J Computer Sci. & Mobile Computing, vol. 5, no. 3, pp. 32–41, 2016.

N. Mehdi and M. Yaghobi, “Designing a fuzzy expert system of diagnosing the hepatitis B intensity rate and comparing it with adaptive neural network fuzzy system”, In Proceedings of the World Congress on Engineering and Computer Science, San Francisco, Calif, USA, October 2009.

V. Ekong, E. Onibere, and A. Imianvan, “Fuzzy cluster means system for the diagnosis of liver diseases,” Int. J Computer Science & Technol., vol. 2, no. 3, pp. 5–12, 2011.

A. A. Imianvan, and J. C. Obi, “Diagnostic evaluation of hepatitis utilizing fuzzy clustering means”, World J Applied Sci & Technol., vol. 3, no. 1, pp. 23–30, 2011.

O. W. Samuel, M. O. Omisore, and B. A. Ojokoh, “A web based decision support system driven by fuzzy logic for the diagnosis of typhoid fever”, Expert System Application, vol. 40, no. 1, pp. 4164–4171, 2013.

A. Imianvan, F. Anosike, and C. Obi, “An expert system for the intelligent diagnosis of HIV using fuzzy cluster means algorithm”, Global J Computer Sci. & Technol., vol. 11, no. 12, pp. 73–80, 2011.

A. Kadhim, A. Alam, and H. Kaur, “Design and implementation of fuzzy expert system for back pain diagnosis”, Int. J Innovation Technol. Creation & Eng., vol. 1, no. 9, pp. 16–22, 2011.

A. A. Imianvan, J. C Obi, and O. I. Ehigiator, “Prototype of a fuzzy cluster means decision support system for the differential diagnosis of arthritis”, J Institute of Mathematics & Computer Sci., vol. 22, no. 2, pp. 135–144, 2011.

J. C. Obi and A. A. Imianvan, “breast cancer recognition using fuzzy classifier”, Int. J Academic Res., vol. 3, no. 3, pp. 449–454, 2011.

A. A. Imianvan and J. C. Obi, “Prototype of fuzzy cluster means system for the diagnosis of diabetis”, Int. J Natural & Appl. Sci, vol. 3, no. 2, pp. 60–72, 2011.

M. D. Okpor, “Prognosis diagnosis of gestational diabetes utilizing fuzzy classifier”, Int. J Computer Sci. & Network Security, vol. 15, no. 6, 2015.

A. A. Imianvan and J. C. Obi, “Prognosis diagnosis of pelvic inflammatory disease utilizing logical fuzzy classifier expert structure”, Scientia Africana, An Int. J Pure and Appl. Sci, vol. 11, no. 1, pp. 25–30, 2012.

J. C. Obi and A. A. Imianvan, “Analysis, diagnosis and prognosis of leprosy utilizing fuzzy classifier”, Bayero J Pure and Appl. Sci., vol. 5, no. 1, pp. 149–154, 2012.

I. B. Ajenaghughrure, P. Sujatha, and M. I. Akazue, “Fuzzy based multi-fever symptom classifier diagnosis model”, Int. J Technol. & Computer Sci., vol. 10, no. 1, pp. 13–28, 2017

V. Pabbi, “Fuzzy expert system for medical diagnosis”, Int. J Scientific and Res. Publications, vol. 5, no. 1, pp. 1–7, 2015.

S. Seth, “MExS a fuzzy rule based medical expert system to diagnose the diseases”, IOSR J Eng., vol. 4, no. 7. pp. 57–62, 2014.

A. K. Anand, R. Kalpana, and S. Vijayalakshmi, “Design and implementation of a fuzzy expert system for detecting and estimating the level of asthma and chronic obstructive pulmonary disease”, Middle-East J Scientific Res., vol. 14, no. 11, pp. 1435–1444, 2013.

J. C. Obi and A. A. Imianvan, “Chronic obstructive pulmonary disease prognosis diagnosis utilizing fuzzy classifier proficient approach”, Nigerian J Sci. & Environ., vol. 12, no. 1, pp. 65–72, 2013.

A. A. Imianvan, O. N. Ogini, and J. C. Obi, “Application of fuzzy classifier to obsessive compulsive disorder identification and prognosis”, Nigerian J Sci. & Environ.,, vol. 12, no. 2, pp. 84–90, 2013.

M. J. P. Castanho, F. Hernandes, A. M. DeRe, S. Rautenberg, and A. Bills, “Fuzzy expert system for predicting pathological stage of prostate cancer”, Expert Systems with Applications, vol. 20, no. 3, pp. 466–470, 2013.

A. A. Imianvan, and J. C. Obi, “Prototype of fuzzy cluster means system for the diagnosis of peptic ulcer”, J Computer Sci., vol. 23, no. 1, pp. 1–8, 2012.

A. A. Imianvan and J. C. Obi, “Cognitive analysis of multiple sclerosis utilizing fuzzy cluster means algorithm”, Int. J Artificial Intelligence & Applications, vol. 3, no. 1, pp. 33–45, 2012.

J. C. Obi, and A. A. Imianvan, “Clustering of data utilizing fuzzy classifier expert system for identification of gonorrhea in men”, Science Research Annals, vol. 5, no. 1, pp. 8–13, 2013.

A. A. Imianvan and J. C Obi, “Diagnostic analysis and prognosis assessment of enteric fever using fuzzy classifier”, Nigeria J Life Sci., vol. 4, no. 1, pp. 82–85, 2014.

J. C. Obi and A. A. Imianvan, “Detection of cat anal gland cancer utilizing a fuzzy graphical approach”, J Nigerian Institution of Production Engineers, vol. 19, no. 1, pp. 111–117, 2015.

S. A. Fatumo, E. Adetiba, and J. O. Onaolapo, “Implementation of XpertMalTyph: an expert system for medical diagnosis of the complications of malaria and typhoid”, IOSR J Computer Eng., vol. 8, no. 5, pp. 34–40, 2013.

S. Tunmibi, O. Adeniji, A. Aregbesola, and D. Ayodeji, “A rule based expert system for diagnosis of fever”, Int. J Advanced Res., vol. 1, no. 7, pp. 343-348, 2013.

M. Patel, A. Patel, and P. Virparia, “Rule based expert system for viral infection diagnosis”, Int. J Advanced Res. Computer Science & Software Eng., vol. 3, no. 5, 2013.

M. S. Hossain, M. S. Khalid, S. Akter, and S. Dey, “A belief rule-based expert system to diagnose influenza”, in proceedings of 9th Int. Forum on Strategic Technology, Bangladesh, November 2014, pp. 113-116.

R. H. Komal, and S. G. Vijay, “Rule-based expert system for the diagnosis of memory loss diseases”, Int. J Innovative Sci. Eng. & Technol., vol. 1, no. 3, pp. 5–14, 2014.

M. A. Hambali, A. A. Akinyemi, and J. D. Luka, “Expert system for Lassa fever diagnosis using rule based approach”, Annals Computer Science Series, vol. 15, no. 2, pp. 68-74, 2017.

R. O. Osaseri, E. A. Onibere, and A. R. Usiobiafo, “Fuzzy expert model for diagnosis of Lassa fever”, J Nigerian Association of Mathematical Physics, vol. 27, no. 1, pp. 533-540, 2014.

S. Karim, H. Suryaningsih, and A. Lause, “Expert system for diagnosing dengue fever”, Seminar National Aplikasi Teknologi Informasi, vol. 1, no. 1, pp. 21–23, 2007.

S. Alshaban and A. K. Taher, ”Building a proposed expert system using blood testing”, J Eng. Technol. Res., vol. 1, no. 1, pp. 1–6, 2009.

R. A. Soltan, M. Z. Rashad, and B. El-Desouku, “Diagnosis of some diseases in medicine via computerized expert system”, Int. J Computer Sci. & Information Technol., vol. 5, no. 5, pp. 79–90, 2013.

K. P. P-Santosh, P. S. Dipti, and M. Indrajit, “An expert system for diagnosis of human diseases”, Int. J Computer Applications, vol. 1, no. 13, pp. 71–73, 2010.

M. O. Omisore, O. W. Samuel, and E. J. Atajeromavwo, “A genetic-neuro-fuzzy inferential model for diagnosis of tuberculosis”, Appl. Computing & Informatics, vol. 13, no. 1, pp. 27–37, 2017.

E. Solanki, A. Amit, and K. P. Chandresh, “Lung cancer detection and classification using curvelet transform and neural network”, Int. J Scientific Res. & Dev., vol. 3, no. 3, pp. 2668–2672, 2015.

A. Akhikpemelo, M. J. E. Evbogbai, and M. S. Okundamiya, “Fault detection on a 132kV transmission line using artificial neural network,” Int. Review of Electrical Eng., vol. 14, no. 3, pp. 220-225, 2019.

T. Manikandan, N. Bharathi, M. Sathish, and V. Asokan, “Hybrid neuro-fuzzy system for prediction of lung disease based on the observed symptom values”, J Chemical & Pharmaceutical Sci., vol. 8, no. 1, pp. 69-76, 2017.

G. Sumana, G. A. Babu, and R. S. Kumar, “Diagnosis of glomerulonephritis by an ANN based on physical symptoms and clinical observations of the blood samples”, In proceedings of the World Congress on Eng., vol. 2, no. 1, pp. 1 – 7, 2013.

R. A. Amapwan and N. V. Blamah, “The application of information technology in medical practices: using artificial neural network for the diagnosis of hepatitis B”, Int. J Informatics, Technol. & Computers, vol. 5, no. 2, pp. 38–47, 2019.

M. R. Raoufy, P. Vahdani, S. M. Alavian, S. Fekri, P. Eftekhari, and S. Gharibzadeh, “A novel method for diagnosing cirrhosis in patients with chronic hepatitis B: artificial neural network approach”, J Medical Systems, vol. 35, no. 1, pp. 121-126, 2011.

M. S. Bascil, and H. Oztekin, “A study on hepatitis disease diagnosis using probabilistic neural network”, J Medical Systems, vol. 36, no. 3, pp. 1603-1606, 2012.

R. Kh, G. M. Rasegh, G. N. Chagha, and J. Haddania, “An intelligent diagnostic system for detection of hepatitis using multi-layer perceptron and colonial competitive algorithm,” J Mathematics & Computer Sci., vol. 4, no. 1, pp. 237–245, 2012.

C. Mahesh, E. Kannan, and M. S. Saravanan, “Generalized regression neural network based expert system for hepatitis b diagnosis”, J Computer Sci., vol. 10, no. 4, pp. 563–570, 2014

K. Rezaee, J. Haddadnia, and M. Rasegh-Ghezelbash, “A novel algorithm for accurate diagnosis of hepatitis B and its severity”, Int. J Hospital Res., vol. 3, no. 1, pp. 1-10, 2014.

D. Panchal and S. Shah, “Artificial intelligence based expert system for hepatitis B diagnosis”, Int. J Modeling and Optimization, vol. 1, no. 4, pp. 362–370, 2011.

S. Ansari, I. Shafi, A. Ansari, J. Ahmad, and S. I. Shah, “Diagnosis of liver disease induced by hepatitis virus using artificial neural networks”, In IEEE 14th Int. Multitopic Conference (INMIC), pp. 8 – 12, 2011.

E. Lopez-Gonza, M. A. Lez, N. Rodrı-guez-Ferna, and C. Mendana-Cuervo, “The logistic decision making in management accounting with genetic algorithms and fuzzy sets”, Mathematics with Soft Computing, vol. 7, no. 1, pp. 229–241, 2000.

R. B. Vathana and R. Balasubbramanian, “Genetic-neuro-fuzzy inferential model for tuberculosis detection”, Int. J Appl. Eng. Res., vol. 13, no. 17, pp. 13308–13312, 2018.

F. I. Amadin and M. E. Bello, “A genetic neuro fuzzy approach for handling the nurse rostering problem”, Pacific J Sci. Technol., vol. 19, no. 1, pp. 198–205, 2018.

I. D. Oladipo and A. O. Babatunde, “Framework for a genetic-neuro-fuzzy inference system for diagnosis of diabetes mellitus”, Anale Seria Informatica, vol. 13, no. 1, pp. 194–201, 2018.

W. Suparta and M. A. Kemal, Modeling of tropospheric delays using ANFIS, Switzerland, Springer International Publishing, 2016.

T. Manikandan, N. Bharathi, M. Sathish, and V. Asokan, “Hybrid neuro-fuzzy system for prediction of lung disease based on the observed symptom values”, J Chemical & Pharmaceutical Sci., vol. 8, no. 1, pp. 69-76, 2017.

M. Nagarajasri and M. Padmavathamma, “Threshold neuro fuzzy expert system for diagnosis of breast cancer”, Int. J Computer Applications, vol. 66, no. 8, pp. 6-10, 2013.

M. Gumpy and I. Goni, “Neuro-fuzzy approach for diagnosing and control of tuberculosis”, Int. J Computational Sci. Information Technol. & Control Eng., vol. 5, no. 1, pp. 1-10, 2018.

I. Goni, C. U. Ngene, I. Manga, N. Auwal, and J. C. Sunday, “Intelligent system for diagnosing tuberculosis using adaptive neuro-fuzzy”, Asian J Res. Computer Sci., vol. 2, no. 1, pp. 1-9, 2018.

A. A. Imianvan and J. C. Obi, “Decision support system for the identification of tuberculosis using neuro-fuzzy logic”, Nigerian Annals of Natural Sci., vol. 12, no. 1, pp. 12–20, 2012.

T. M. Oladele, C. D. Okonji, A. Adekanmi, and F. F. Abiola, “Neuro-fuzzy expert system for diagnosis of thyroid diseases”, Annale Computer Science Series, vol. 16, no. 2, pp. 45-54, 2018.

E. P. Ephzibah and V. Sundarapandian, “A neuro fuzzy expert system for heart disease diagnosis”, Computer Sci. & Eng.: An Int. J, vol. 2, no. 1, pp. 17-23, 2012.

M. E. Shaabani, T. Banirostam, and A. Hedayati, “Implementation of neuro fuzzy system for diagnosis of multiple sclerosis”, Int. J Computer Sci. & Network, vol. 5, no. 1, pp. 157-164, 2016.

A. O. Egwali and J. C. Obi, “An adaptive neuro-fuzzy inference system for diagnosis of EHF,” Pacific J Sci. & Technol., vol. 16, no. 1, pp. 251-261, 2015.

J. J. Tom and N. P. Anebo, “A neuro-fuzzy based model for diagnosis of monkey pox diseases”, Int. J Computer Sci. Trends & Technol., vol. 6, no. 2, pp. 143–153, 2018.

S. Maskara, A. Kushwaha, and S. Bhardwaj, “Adaptive euro fuzzy expert system for disease diagnosis”, Int. J Innovations Eng. & Technol., vol. 10, no. 2, pp. 121-123, 2018.

J. C. Obi and A. A. Imianvan, “interactive neuro-fuzzy expert system for diagnosis of leukemia”, Global J Computer Sci. & Technol., vol. 11, no. 12, pp. 43–50, 2011.

A. A. Imianvan and J. C. Obi, “Diagnosis analysis of bipolar disorder using neuro-fuzzy logic”, World J Appl. Sci. & Technol., vol. 3, no. 11, pp. 63–72, 2011.

J. C. Obi, and A. A. Imianvan, “Decision support system for the intelligent identification of alzheimer using neuro-fuzzy logic”, Int. J Soft Computing, vol. 2, no. 2, pp. 25–38, 2011.

J. C. Obi and A. A. Imianvan, “Decision support system for the diagnosis of malaria using neuro-fuzzy logic”, Int. J Natural & Appl. Sci., vol. 3, no. 2, pp. 36–49, 2011.

J. C. Obi, and A. A. Imianvan, “Fuzzy-neural approach for colon cancer prediction”, Scienta Africana, An Int. J Pure & Appl. Sci., vol. 11, no. 1, pp. 65–76, 2012.

A. A. Imianvan and J. C. Obi, “Application of neuro-fuzzy expert system for the probe and prognosis of thyroid disorder”, Int. J Fuzzy Logic System, vol. 2, no. 2, pp. 1–11, 2012.

A. Imianvan and J. C. Obi, “Intelligent neuro-fuzzy expert system for autism recognition”, Nigerian J Sci. & Environ., vol. 12, no. 1, pp. 73–80, 2013.

J. Jang, “ANFIS: adaptive-network-based fuzzy inference system”, IEEE Transactions on Systems, Man, and Cybernetics, vol. 23, no. 3, pp. 665–685, 1993.

S. Sebhatu, A. Kumar, and S. Pooja, “Applications of soft computing techniques for pulmonary tuberculosis diagnosis”, Int. J Recent Technol. & Eng., vol. 8, no. 3, pp. 1–9, 2019.

J. C. Obi, A. A. Imianvan, and V. E. Ekong, “Genetic neuro-fuzzy system for the intelligent recognition of stroke”, J Computer Sci. Application, vol. 19, no. 1, pp. 24 – 31, 2012.

J. C. Obi and A. A. Imianvan, “Soft computing: a objective approach in varied diabetes recognition”, J Biomedical Eng. & Medical Imaging, vol. 1, no. 5, pp. 23 – 33, 2014.

R. Kavita and B. Kavita, “A soft computing genetic-neuro fuzzy approach for data mining and its application to medical diagnosis”, Int. J Eng. & Advanced Technol., vol. 3, no. 1, pp. 5–8, 2013.

M. A. Alghamdi, S. G. Bhind, and M. A. Alam, “disease diagnosis using soft computing model: a digest”, Int. J Computer Applications, vol. 102, no. 10, pp. 1–3, 2014.

M. Al-Akhras, A. Barakat, M. Alawaidhi, and M. Habib, “Using soft computing techniques to diagnose glaucoma disease”, J Infection & Public Health, vol. 1, no. 1, pp. 1–8, 2019.

A. Pratap, C. S. Kanimozhiselvi, R. Vijayakumar, and K. V. Pramod, “Soft computing models for the predictive grading of childhood autism: a comparative study”, Int. J Soft Computing & Eng., vol. 4, no. 3, pp. 64 – 67, 2014.

K. S. Parikh, T. P. Shah, R. Kota, and R. Vora, “Diagnosing common skin diseases using soft computing techniques”, Int. J Bio-Science & Bio-Technol., vol. 7, no. 6, pp. 275–286, 2015.

V. Sivakrithika, S. S Merlin, and K. Sugirtha, “An efficient medical image diagnosis system using soft computing techniques”, J Theoretical & Applied Information Technol., vol. 36, no. 2, pp. 190–198, 2012.

A. Mahdieh, B. Nooshin, and A. Karim, “New hybrid hepatitis diagnosis system based on genetic algorithm and adaptive network fuzzy inference system”, In 21st IEEE Iranian conference on electrical engineering (ICEE), 1 – 6, 2013

A. Waheed, A. Ayaz, I. Amjad, H. Muhammad, H. Anwar, R. Gauhar, K. Salman, U. K. Ubaid, K. Dawar, and H. Lican, “Intelligent hepatitis diagnosis using adaptive neuro-fuzzy inference system and information gain method”, Soft Computing, vol. 1, no. 1, 1–8, 2018.

C. Hui-Ling, L. Da-You, Y. Bo, L. Jie, and W. Gang, “A new hybrid method based on local fisher discriminant analysis and Support Vector Machines for Hepatitis disease diagnosis”, Expert Systems with Applications, vol. 38, no. 9, pp. 11796–11803, 2011.

N. Mehdi, M. Azra, R. Mina, and J. Hassan, “Diagnosing Hepatitis disease by using Fuzzy Hopfield Neural Network”, Annual Research and Review in Biology, pp. 2709–2721, 2014.

E. Dogantekin, A. Dogantekin, and D. Avci, “Automatic hepatitis diagnosis system based on linear discriminant analysis and adaptive network based on fuzzy inference system”, Expert System Application, vol. 36, no. 1, pp. 11282–11286, 2009.

D. Calisir and E. Dogantekin, “A new intelligent hepatitis di¬agnosis system: PCA LSSVM”, Expert System Application, vol. 38, no. 10, pp. 10705–10708, 2011.

L. Ozyılmaz and T. Yıldırım, “Artificial neural networks for diag¬nosis of hepatitis disease”, In Int. Joint Conference on Neural Networks, vol. 1, pp. 586–589, 2003.

K. Polat and S. Gunes, “Hepatitis disease diagnosis using a new hybrid system based on feature selection and artificial immune recognition system with fuzzy resource allocation”, Digital Signal Process, vol. 16, pp. 889–901, 2006.

J. S. Sartakhti, M. H. Zangooei, and K. Mozafari, “Hepatitis disease diagnosis using a novel hybrid method based on support vector machine and simulated annealing”, Computer Methods & Programs in Biomedicine, vol. 108, no. 2, pp. 570–579, 2012.

N. Khorashadizade and H. Rezaei, “New method for rapid diagnosis of Hepatitis disease based on reduction feature and machine learning”, J Advanced Computer Sci. & Technol., vol. 4, no. 1, pp. 148-155, 2015.

B. Karlik, “Hepatitis disease diagnosis using backpropagation and the naive bayes classifiers”, BURCH J Sci. & Technol., vol. 1, no. 1, pp. 4962–4971, 2011.

M. Neshat, M. Sargolzaei, A. N. Toosi, and A. Masoumi, “Hepatitis disease diagnosis using hybrid case based reasoning and particle swarm optimization”, ISRN Artificial Intelligence, vol. 2012, doi: 10.5402/2012/609718

N. Mehrbakhsh, A. Hossein, S. Leila, I. Othman, and A. Elnaz, “A predictive method for hepatitis disease diagnosis using ensembles of neuro-fuzzy technique”, J Infection & Public Health, vol. 12, no. 1, pp. 13–20, 2019.

K. Yılmaz and U. Murat, “A hybrid decision support system based on rough set and extreme learning machine for diagnosis of hepatitis disease”, Appl. Soft Computing, vol. 13, no. 8, pp. 3429–3438, 2013.

G. Sathyadevi, “Application of CART algorithm in hepatitis disease diagnosis”, Int. Conference on Recent Trends in Information Technology, Chennai, Tamil Nadu, pp. 1283-1287, 2011.

A. H. Roslina and A. Noraziah, “Prediction of hepatitis prognosis using support vector machine and wrapper method”, 2010 Seventh Int. Conference on Fuzzy Systems and Knowledge Discovery, Yantai, pp. 2209-2211, 2010.

S. Shariati and M. M. Haghighi, “Comparison of ANFIS neural network with several other ANNs and support vector machine for diagnosing hepatitis and thyroid diseases”, Int. Conference on Computer Information Systems and Industrial Management Applications (CISIM), Krackow pp. 596–599, 2010.

I. Rahmon, O. Omotosho, and F. Kasali, “Diagnosis of hepatitis using adaptive neuro-fuzzy inference system”, Int. J Computer Applications, vol. 180, no. 38, pp. 46–53, 2018.

L. Ozyılmaz and T. Yıldırım, “Artificial neural networks for diag¬nosis of hepatitis disease”, In Int. Joint Conference on Neural Networks, vol. 1, no. 1, pp. 586–589, 2003.

Ç. Onursal, T. Feyzullah, and G. Şenol, “An application of multilayer neural network on hepatitis disease diagnosis using approximations of sigmoid activation function”, Dicle Medical J, vol. 42, no. 2, pp. 150-157, 2015.

A. Gulzar, A. K. Muhammad, A. Sagheer, A. Atifa, S. K. Bilal, and S. A. Muhammad, “Automated diagnosis of hepatitis B using multilayer mamdani fuzzy inference system”, J Healthcare Eng., vol. 1, no 1., 2019

U. S. Ogah, P. B. Zirra, and O. Sarjiyus, “Knowledge based system design for diagnosis of hepatitis B virus using generalized regression neural network”, American J Computing & Eng., vol. 1, no. 1, pp. 1-19, 2017.

N. Mehdi and M. Yaghobi, “Designing a fuzzy expert system of diagnosing the hepatitis B intensity rate and comparing it with adaptive neural network fuzzy system,” In Proceedings of the World Congress on Engineering and Computer Science, San Francisco, Calif, USA, October 2009.

C. Mahesh, V. G. Suresh, and B. Manjula, “Diagnosing hepatitis B using artificial neural network based expert system”, Int. J Eng. & Innovative Technol., vol. 3, no. 6, pp. 139–144, 2013.

T. A. Jilani, H. Yasin, and M. M. Yasin, “PCA-ANN for classification of hepatitis-C patients”, Int. J Computer Applications, vol. 14, no. 7, pp. 56 – 67, 2011.

*Journal of Advances in Science and Engineering*,

*3*(1), 43-56. https://doi.org/10.37121/jase.v3i1.83

Copyright (c) 2020 Adetokunbo MacGregor John-Otumu, Godswill U. Ogba , Obi C. Nwokonkwo

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