A Clinical Prognostic Framework for Classifying Severe Liver Disorders (SLDs) and Lungs’ Vulnerability to Virus
Most severe liver diseases (SLDs) are attributed to increased risk for cancer, and cirrhosis, through which the manifestation of fibrotic tissues and scars tends to affect liver function The role of liver is indispensable, as inner organ performing services that ranges from metabolism, immune guide, energy producer and digestive aid, just to mention a few. Prevalence of classification problem and the need for automated prognosis is the continual drive to apply data mining techniques and/or machine learning algorithms in medical diagnosis and clinical support systems. Computational scientists and researchers in the field of artificial intelligence have recorded notable efforts with existing methods/models for diagnosis or prognosis, yet their effectiveness and functional performance is not without drawback due to ambiguity of medical information and selected features in patients’ data to tell the future course. In this paper, a novel hybridized machine learning model was provided (Fuzzy c-BC) for clinical classification of Severe Liver Disorders (SLDs) and to determine Lungs Vulnerability (LV) to virus; by incorporating individual strength of fuzzy cluster means (FCM) and naive Bayes classifier (NBC) for projecting future course of every categorized liver disease (LD) and its implication to aggravate lungs infection if preventive measures are not taken in timely manner.
Keywords:Prognosis, Liver, Lungs, Virus
K., Ayushi, A., Sharma, V., Sharma, R, Gupta, “Liver Disease Prognosis Based on Clinical Parameters using Machine Learning Approach”. International Journal for Research in Applied Science and Engineering Technology (IJRASET), Vol. 5, No. 6, pp. 13-20, 2017.
L., Anand, V., Neelanarayanan, “Feature Selection for Liver Disease using Particle Swarm Optimization Algorithm”. International Journal of Recent Technology and Engineering (IJRTE), Vol. 8, No. 3, pp. 24-30, 2019.
T., Claburn, “Looming Ventilator Shortage amid Pandemic Sparks Rise of Open Source DIY Medical Kit”. Retrieved from https://www.theregister.co.uk/2020 on 18/03/2020.
K.C, Cheng, W.Y, Lin, C.S, Liu, … S.W, Lai, “Association of Different Types of Liver Diseases with Demographic and Clinical Factors.” Journal of BioMedicine, Vol. 6, No. 3, pp. 16-22, 2016.
C.I, Ejiofor, C., Ugwu, “Application of Support Vector Machine and Fuzzy Logic for Detecting and Identifying Liver Disorder in Patients”. IOSR Journal of Computer Engineering, Vol. 17, No. 3, pp. 50-53.
V.E, Ekong, E.A, Onibere, A.A, Imianvan, “An Expert System for the Diagnosis of Liver Diseases using Fuzzy Cluster Means”. Journal of Computer Science and its Applications. Vol. 18, No. 2, pp. 55-65, 2011.
A.A, Imianvan, J.C, Obi, “Fuzzy Cluster Means Expert System for the Diagnosis of Tuberculosis”. Global Journal of Computer Science and Technology. Vol. 11, No. 6, pp. 36-43, 2011.
S.A, Karthik, J, Priyadarishini, B.K, Tripathy, “Classification and Rule Extraction using Rough Set for Diagnosis of Liver Disease and its Types”. Journal of Advances in Applied Science, Vol. 2, No. 3, pp 334-345.
A., Lamesgin M., Sirajudeen, “Implementation of an Expert System for Lung Disease Diagnosis”. Retrieved 27/03/2019 from https://www.researchgate.net/303699292 2016.
S., Mani, R., Turaga, S.C, Xue, … J.J, Yang, “Early Detection and Staging of Chronic Liver Diseases with a Protein MRI Contrast Agent”. Available at https://doi.org/10.1038/s41467-019-11984-2, 2018.
B.A, Mashhour, “Assessment of Liver Function using Hybrid Neuro-Fuzzy Model of Blood Albumin”. International Journal of Healthcare Information System and informatics. Vol. 5, No. 4, pp. 49-59, 2010.
S., Prem-Pal, S., Ranjit, S., “Design of a Clinical Decision Support System using cased-based Reasoning”. In the proceedings of International Multidisciplinary Conference on Research, Developments and Practices in STEAMS. Issue 4, pp. 27-40, 2013.
G., Rajathi, G., Jiji, “Chronic Liver Disease Classification using Hybrid Whale Optimization with Simulated Annealing and Ensemble Classifier”, Vol. 11, No.33, pp. 2-12, 2013.
M., Rajabi, H., Sadeghizadeh, Z., Mola-Amini, N., Ahmadyrad,, ”Hybrid Adaptive Neuro Fuzzy Inference System for Diagnosing the Liver Disorders”, 2017.
I., Rahmon, O., Omotosho, F., Kasali, “Diagnosis of Hepatitis using Adaptive Neuro-Fuzzy Inference System (ANFIS)”. International Journal of Computer Applications, Vol. 108, No. 38, pp. 45-55, 2018.
R., Rajamani, M., Rathika, “Analysis of Liver Cancer using Adaptive Neuro Fuzzy Inference System”. International Journal of Innovative Research in Computer and Communication Engineering. Vol. 3, No. 7, pp. 7-13, 2015.
S.L, Sharmila, C, Dharnma, P., Venkatesan, P., “Disease Classification using Machine Learning ,Algorithms – A Comparative Study”. International Journal of Pure and Applied Mathematics. Vol. 114, No. 6, pp. 1-10, 2017.
D.M, Souran, H, Hatamiran, V.E, Bales, “Classification of the Liver Disorders Data using Multi-Layer Adaptive Neuro-Fuzzy Inference System”. Retrieved from https://www.researchgate.net/publication/304297465 on 12th March, 2020.
J., Singla, G, Dinesh, B., Abhinav, “Medical Expert Systems for Diagnosis of Diseases. International Journal of Computer Applications, Vol. 3, No. 7, pp. 75-87, 2014.
H., Vaidya, S.K, Chandhari, H.T, Ingale, “Neuro Fuzzy Based Liver Disease Classification”. Available at www.ijariie.com, 2017.
Watch, “COVID-19: Nigeria Records Third Case of Coronavirus”. Retrieved from https://m.youtube.com/watch on 24th March, 2020.
Wikipedia, “Understanding the computational intelligence and machine learning.” Retrieved on 4th March, 2019 from http://www.google.com, 2019.
X., Zhang, H., Zhao, S., Zhang, R. Li, ”A Novel Deep Neural Network Model for Multi-Label Chronic Disease Prediction”. Vol. 10, No. 5, pp. 1-8. Available at 10.3389/fgene.2019.00351, 2019.
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