Intelligent Medical Diagnostic System for Hepatitis B
Source abstract
Abstract
The hepatitis B virus is the most deadly virus, which significantly affects the human liver. The termination of the hepatitis B virus is mandatory and can be done by taking precautions as well as a suitable cure in its introductory stage; otherwise, it will become a severe problem and make a human liver suffer from the most dangerous diseases, such as liver cancer. In this paper, two medical diagnostic systems are developed for the diagnosis of this life-threatening virus. The methodologies used to develop these models are fuzzy logic and the neuro-fuzzy technique. The diverse parameters that assist in the evaluation of performance are also determined by using the observed values from the proposed system for both developed models. The classification accuracy of a multilayered fuzzy inference system is 94%. The accuracy with which the developed medical diagnostic system by using Adaptive Network based Fuzzy Interference System (ANFIS) classifies the result corresponding to the given input is 95.55%. The comparison of both developed models on the basis of their performance parameters has been made. It is observed that the neuro-fuzzy technique-based diagnostic system has better accuracy in classifying the infected and non-infected patients as compared to the fuzzy diagnostic system. Furthermore, the performance evaluation concluded that the outcome given by the developed medical diagnostic system by using ANFIS is accurate and correct as compared to the developed fuzzy inference system and also can be used in hospitals for the diagnosis of Hepatitis B disease. In other words, the adaptive neuro-fuzzy inference system has more capability to classify the provided inputs adequately than the fuzzy inference system.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: COVID-19 diagnosis using AI · Artificial Intelligence in Healthcare · Digital Imaging for Blood Diseases
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (68)
Show source-provided references
1. System dynamics of chronic hepatitis B - modeling the virus and immune system of chronic hepatitis b
2. Multiple granular analysis of TCM data with applications on diagnosis of hepatitis B
3. Hepatitis B virus infection10.1016/S0140-6736(09)60207-5 ↗
4. Chronic hepatitis B infection10.1001/jama.2018.3795 ↗
5. A comparison of multimodal biomarkers for chronic hepatitis B assessment using recursive feature elimination
6. Discovering clinical biomarkers of chronic hepatitis B by mining mutation hotspots
7. A mathematical model for acute hepatitis B virus infection
8. Detection of hepatitis viruses based on J48, KStar and naïve Bayes classifier
9. An optimized stacking ensemble model for phishing websites detection10.3390/electronics10111285 ↗
10. A novel hybrid deep learning model for detecting COVID-19-related rumors on social media based on LSTM and concatenated parallel CNNs10.3390/app11177940 ↗
11. Data mining on DNA sequences of hepatitis B virus10.1109/TCBB.2009.6 ↗
12. Hepatitis C classification using data mining techniques10.5120/2934-3888 ↗
13. Random forest and Bayesian prediction for hepatitis B virus reactivation
14. Diagnosis based on decision tree and discrimination analysis for chronic hepatitis b in TCM
15. Chronic hepatitis classification using SNP data and data mining techniques
16. Enhancing Parkinson's disease prediction using machine learning and feature selection methods10.32604/cmc.2022.023124 ↗
17. Combining CNN and grad-cam for COVID-19 disease prediction and visual explanation10.32604/iasc.2022.022179 ↗
18. Phishing websites detection by using optimized stacking ensemble model10.32604/csse.2022.020414 ↗
19. An ensemble learning based approach for detecting and tracking COVID 19 rumors10.32604/cmc.2022.018972 ↗
20. Servicing your requirements: An FCA and RCAdriven approach for semantic web services composition10.1109/ACCESS.2020.2982592 ↗
21. Hepatitis B diagnosis using logical inference and generalized regression neural networks
22. The diagnosis of hepatitis diseases by support vector machines and artificial neural networks
23. Wang · Interdisciplinary Computing in Java Programming · 2003 · 8110.1007/978-1-4615-0377-4_5 ↗
24. Artificial neural networks, back propagation, and the Kelley-Bryson gradient procedure10.2514/3.25422 ↗
25. The perceptron: A probabilistic model for information storage and organization in the brain10.1037/h0042519 ↗
26. Artificial neural systems10.1016/0019-0578(90)90024-F ↗
27. Introduction to artrificial neural network
28. Fuzzy logic
29. Which logic is the real fuzzy logic?10.1016/j.fss.2005.10.010 ↗
30. A fuzzy medical diagnostic support system for cardiovascular diseases diagnosis using risk factors
31. ANFIS: Adaptive-network-based fuzzy inference system,”10.1109/21.256541 ↗
32. Deep learning-based rumor detection on microblogging platforms: A systematic review10.1109/ACCESS.2019.2947855 ↗
33. A Neuro-fuzzy based intelligent system for diagnosis of renal cancer
34. An intelligent diagnosis system for diabetes on linear discriminant analysis and adaptive network based fuzzy inference system: LDA-ANFIS10.1016/j.dsp.2009.10.021 ↗
35. Comparative study of adaptive neuro-fuzzy and fuzzy inference system for diagnosis of hypertension
36. Diagnosing hepatitis B using artificial neural network based expert system
37. Concatenation of pre-trained convolutional neural networks for enhanced COVID-19 screening using transfer learning technique10.3390/electronics11010103 ↗
38. Phishing websites detection by using optimized stacking ensemble model10.32604/csse.2022.020414 ↗
39. Machine-learning-based classification of real-time tissue elastography for hepatic fibrosis in patients with chronic hepatitis B10.1016/j.compbiomed.2017.07.012 ↗
40. Clinical prediction of HBV and HCV related hepatic fi brosis using machine learning10.1016/j.ebiom.2018.07.041 ↗
41. A predictive method for hepatitis disease diagnosis using ensembles of neuro-fuzzy technique10.1016/j.jiph.2018.09.009 ↗
42. Analysis of hepatitis B virus infection in blood sera using Raman spectroscopy and machine learning10.1016/j.pdpdt.2018.05.010 ↗
43. Predicting hepatitis B virus–positive metastatic hepatocellular carcinomas using gene expression profiling and supervised machine learning10.1038/nm843 ↗
44. An improved sentiment classification approach for measuring user satisfaction toward governmental services’ mobile apps using machine learning methods with feature engineering and SMOTE technique10.3390/app12115547 ↗
45. Small object detection via precise region-based fully convolutional networks10.32604/cmc.2021.017089 ↗
46. J. Wang, Y. Wu, S. He, P. K. Sharma, X. Yu et al., “Lightweight single image super-resolution convolution neural network in portable device,” KSII Transactions on Internet and Information Systems (TIIS), vol. 15, no. 11, pp. 4065–4083, 2021.10.3837/tiis.2021.11.011 ↗
47. Research on recurrent neural network based crack opening prediction of concrete dam
48. Visual object tracking based on residual network and cascaded correlation filters10.1007/s12652-020-02572-0 ↗
49. Parameters compressing in deep learning10.32604/cmc.2020.06130 ↗
50. Automatic leukaemia segmentation approach for blood cancer classification using microscopic images10.32604/cmc.2022.030879 ↗
51. A review on deep learning techniques for IoT data10.3390/electronics11101604 ↗
52. Using CFW-net deep learning models for X-ray images to detect COVID-19 patients10.2991/ijcis.d.201123.001 ↗
53. An advanced deep residual dense network (DRDN) approach for image super-resolution10.2991/ijcis.d.191209.001 ↗
54. Woodland labeling in chenzhou, China, via deep learning approach10.2991/ijcis.d.200910.001 ↗
55. Energy efficient design of massive MIMO by considering the effects of nonlinear amplifiers10.3390/en11051045 ↗
56. Enhancing the energy efficiency of mmWave massive MIMO by modifying the RF circuit configuration10.3390/en12224356 ↗
57. On the energy efficiency of millimeter wave massive MIMO based on hybrid architecture10.3390/en12112227 ↗
58. Energy efficient design of massive MIMO by incorporating with mutual coupling
59. Energy efficient design of massive MIMO based on closely spaced antennas: Mutual coupling effect10.3390/en11082029 ↗
60. QoE-Aware self-tuning of service priority factor for resource allocation optimization in LTE network10.1109/TVT.2019.2952568 ↗
61. Estimating and synthesizing QoE based on QoS measurement for improving multimedia services on cellular networks using ANN method10.1109/TNSM.2019.2946091 ↗
62. A Cost-efficient environment monitoring robotic vehicle for smart industries10.32604/cmc.2022.020903 ↗
63. A Cost-efficient radiation monitoring system for nuclear sites: Designing and implementation10.32604/iasc.2022.022958 ↗
64. Crop yield prediction using machine learning approaches on a wide spectrum10.32604/cmc.2022.027178 ↗
65. An innovative approach utilizing binary-view transformer for speech recognition task10.32604/cmc.2022.024590 ↗
66. Brain tumor segmentation using multi-view attention based ensemble network10.32604/cmc.2022.024316 ↗
67. Applying machine learning approaches for network traffic forecasting10.21817/indjcse/2022/v13i2/221302188 ↗
68. A. A. Khan, “Energy efficient design of 5G massive mimo,” Ph.D. dissertation, Suranaree University of Technology, Thailand, 2018.