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Kharya S, Soni S, Pati A, Panigrahi A, Giri J, Qin H, Mallik S, Nayak DSK, Swarnkar T. Weighted Bayesian Belief Network for diabetics: a predictive model. Front Artif Intell 2024; 7:1357121. [PMID: 38665371 PMCID: PMC11043522 DOI: 10.3389/frai.2024.1357121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 03/27/2024] [Indexed: 04/28/2024] Open
Abstract
Diabetes is an enduring metabolic condition identified by heightened blood sugar levels stemming from insufficient production of insulin or ineffective utilization of insulin within the body. India is commonly labeled as the "diabetes capital of the world" owing to the widespread prevalence of this condition. To the best of the authors' last knowledge updated on September 2021, approximately 77 million adults in India were reported to be affected by diabetes, reported by the International Diabetes Federation. Owing to the concealed early symptoms, numerous diabetic patients go undiagnosed, leading to delayed treatment. While Computational Intelligence approaches have been utilized to improve the prediction rate, a significant portion of these methods lacks interpretability, primarily due to their inherent black box nature. Rule extraction is frequently utilized to elucidate the opaque nature inherent in machine learning algorithms. Moreover, to resolve the black box nature, a method for extracting strong rules based on Weighted Bayesian Association Rule Mining is used so that the extracted rules to diagnose any disease such as diabetes can be very transparent and easily analyzed by the clinical experts, enhancing the interpretability. The WBBN model is constructed utilizing the UCI machine learning repository, demonstrating a performance accuracy of 95.8%.
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Affiliation(s)
- Shweta Kharya
- Department of Computer Science and Engineering, Bhilai Institute of Technology, Durg, Chhattisgarh, India
| | - Sunita Soni
- Department of Computer Science and Engineering, Bhilai Institute of Technology, Durg, Chhattisgarh, India
| | - Abhilash Pati
- Department of Computer Science and Engineering, Siksha ‘O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India
| | - Amrutanshu Panigrahi
- Department of Computer Science and Engineering, Siksha ‘O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India
| | - Jayant Giri
- Department of Mechanical Engineering, Yeshwantrao Chavan College of Engineering, Nagpur, India
| | - Hong Qin
- Department of Computer Science and Engineering, University of Tennessee at Chattanooga, Chattanooga, TN, United States
| | - Saurav Mallik
- Department of Environmental Health, Harvard T H Chan School of Public Health, Boston, MA, United States
| | - Debasish Swapnesh Kumar Nayak
- Department of Computer Science and Engineering, Siksha ‘O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India
| | - Tripti Swarnkar
- Department of Computer Science and Engineering, Siksha ‘O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India
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Nayak DSK, Mahapatra S, Routray SP, Sahoo S, Sahoo SK, Fouda MM, Singh N, Isenovic ER, Saba L, Suri JS, Swarnkar T. aiGeneR 1.0: An Artificial Intelligence Technique for the Revelation of Informative and Antibiotic Resistant Genes in Escherichia coli. FRONT BIOSCI-LANDMRK 2024; 29:82. [PMID: 38420832 DOI: 10.31083/j.fbl2902082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 12/07/2023] [Accepted: 01/12/2024] [Indexed: 03/02/2024]
Abstract
BACKGROUND There are several antibiotic resistance genes (ARG) for the Escherichia coli (E. coli) bacteria that cause urinary tract infections (UTI), and it is therefore important to identify these ARG. Artificial Intelligence (AI) has been used previously in the field of gene expression data, but never adopted for the detection and classification of bacterial ARG. We hypothesize, if the data is correctly conferred, right features are selected, and Deep Learning (DL) classification models are optimized, then (i) non-linear DL models would perform better than Machine Learning (ML) models, (ii) leads to higher accuracy, (iii) can identify the hub genes, and, (iv) can identify gene pathways accurately. We have therefore designed aiGeneR, the first of its kind system that uses DL-based models to identify ARG in E. coli in gene expression data. METHODOLOGY The aiGeneR consists of a tandem connection of quality control embedded with feature extraction and AI-based classification of ARG. We adopted a cross-validation approach to evaluate the performance of aiGeneR using accuracy, precision, recall, and F1-score. Further, we analyzed the effect of sample size ensuring generalization of models and compare against the power analysis. The aiGeneR was validated scientifically and biologically for hub genes and pathways. We benchmarked aiGeneR against two linear and two other non-linear AI models. RESULTS The aiGeneR identifies tetM (an ARG) and showed an accuracy of 93% with area under the curve (AUC) of 0.99 (p < 0.05). The mean accuracy of non-linear models was 22% higher compared to linear models. We scientifically and biologically validated the aiGeneR. CONCLUSIONS aiGeneR successfully detected the E. coli genes validating our four hypotheses.
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Affiliation(s)
- Debasish Swapnesh Kumar Nayak
- Department of Computer Science & Engineering, Institute of Technical Education and Research, Siksha 'O' Anusandhan Deemed to be University, 751030 Bhubaneswar, India
| | - Saswati Mahapatra
- Department of Computer Application, Institute of Technical Education and Research, Siksha 'O' Anusandhan Deemed to be University, 751030 Bhubaneswar, India
| | - Sweta Padma Routray
- Center of Biotechnology, Siksha 'O' Anusandhan Deemed to be University, 751030 Bhubaneswar, India
| | - Swayamprabha Sahoo
- Center of Biotechnology, Siksha 'O' Anusandhan Deemed to be University, 751030 Bhubaneswar, India
| | - Santanu Kumar Sahoo
- Department of Electronics and Communication Engineering, Institute of Technical Education and Research, Siksha 'O' Anusandhan Deemed to be University, 751030 Bhubaneswar, India
| | - Mostafa M Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA
| | - Narpinder Singh
- Department of Food Science and Technology, Graphic Era, Deemed to be University, 248002 Dehradun, India
| | - Esma R Isenovic
- Department of Radiobiology and Molecular Genetics, National Institute of The Republic of Serbia, University of Belgrade, 11000 Belgrade, Serbia
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09128 Cagliari, Italy
| | - Jasjit S Suri
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA
- Department of Food Science and Technology, Graphic Era, Deemed to be University, 248002 Dehradun, India
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
- Knowledge Engineering Center, Global Biomedical Technologies, Inc., Roseville, CA 95661, USA
| | - Tripti Swarnkar
- Department of Computer Application, Institute of Technical Education and Research, Siksha 'O' Anusandhan Deemed to be University, 751030 Bhubaneswar, India
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Panigrahi S, Nanda BS, Bhuyan R, Kumar K, Ghosh S, Swarnkar T. Classifying Histopathological Images of Oral Squamous Cell Carcinoma using Deep Transfer Learning. Heliyon 2023; 9:e13444. [PMID: 37101475 PMCID: PMC10123069 DOI: 10.1016/j.heliyon.2023.e13444] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 08/23/2022] [Accepted: 01/30/2023] [Indexed: 02/08/2023] Open
Abstract
Oral cancer is a prevalent malignancy that affects the oral cavity in the region of head and neck. The study of oral malignant lesions is an essential step for the clinicians to provide a better treatment plan at an early stage for oral cancer. Deep learning based computer-aided diagnostic system has achieved success in many applications and can provide an accurate and timely diagnosis of oral malignant lesions. In biomedical image classification, getting large training dataset is a challenge, which can be efficiently handled by transfer learning as it retrieves the general features from a dataset of natural images and adapted directly to new image dataset. In this work, to achieve an effective deep learning based computer-aided system, the classifications of Oral Squamous Cell Carcinoma (OSCC) histopathology images are performed using two proposed approaches. In the first approach, to identify the best appropriate model to differentiate between benign and malignant cancers, transfer learning assisted deep convolutional neural networks (DCNNs), are considered. To handle the challenge of small dataset and further increase the training efficiency of the proposed model, the pretrained VGG16, VGG19, ResNet50, InceptionV3, and MobileNet, are fine-tuned by training half of the layers and leaving others frozen. In the second approach, a baseline DCNN architecture, trained from scratch with 10 convolution layers is proposed. In addition, a comparative analysis of these models is carried out in terms of classification accuracy and other performance measures. The experimental results demonstrate that ResNet50 obtains substantially superior performance than selected fine-tuned DCNN models as well as the proposed baseline model with an accuracy of 96.6%, precision and recall values are 97% and 96%, respectively.
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Kharya S, Soni S, Swarnkar T. Fuzzy weighted Bayesian belief network: a medical knowledge-driven Bayesian model using fuzzy weighted rules. Int J Inf Technol 2023; 15:1117-1125. [PMID: 36686962 PMCID: PMC9838277 DOI: 10.1007/s41870-022-01153-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 12/27/2022] [Indexed: 01/13/2023]
Abstract
In this current work, Weighted Bayesian Association rules using the Fuzzy set theory are proposed with the new concept of Fuzzy Weighted Bayesian Association Rules to design and develop a Clinical Decision Support System on the Bayesian Belief Network, which is an appropriate area to work in Clinical Domain as it has a higher degree of unpredictability and causality. Weighted Bayesian Association rules to construct a Bayesian network are already proposed. A "Sharp boundary" issue related to quantitative attribute domains may cause erroneous predictions in medicine and treatment in the medical environment. So to eradicate sharp boundary problems in the medical field, the fuzzy theory is applied in attributes to deal with real-life situations. A new algorithm is designed and implemented in this paper to set up a new Bayesian belief network using the concept of Fuzzy Weighted Association rule mining under the Predictive Modeling paradigm named Fuzzy weighted Bayesian belief network using numerous clinical datasets with outshone results.
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Affiliation(s)
- Shweta Kharya
- Department of CSE, Bhilai Institute of Technology, Durg, 491001 India
| | - Sunita Soni
- Department of CSE, Bhilai Institute of Technology, Durg, 491001 India
| | - Tripti Swarnkar
- Department of Computer Applications, S‘O’A Deemed to Be University, Bhubaneshwar, 751001 India
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Rahman J, Panda S, Panigrahi S, Mohanty N, Swarnkar T, Mishra U. Perspective of nuclear fractal dimension in diagnosis and prognosis of oral squamous cell carcinoma. J Oral Maxillofac Pathol 2022; 26:127. [PMID: 35571291 PMCID: PMC9106250 DOI: 10.4103/jomfp.jomfp_470_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 10/24/2021] [Indexed: 12/03/2022] Open
Abstract
Background: Owing to the restricted predictive value of conventional prognostic factors and the inconsistent treatment strategies, several oral squamous cell carcinoma (OSCC) patients are still over-treated or under-treated. In recent years, computer-assisted nuclear fractal dimension (nFD) has emerged as an objective approach to predict the outcome of OSCC. Objective: This study is an attempt to find out the differences in nFD values of epithelial cells of normal tissue, fibroepithelial hyperplasia, verrucous carcinoma, and OSCC. Further effort to evaluate the predictive potential of nFD of tumor cells for cervical lymph node metastasis (cLNM) was also assessed. Methodology: Formalin-fixed paraffin-embedded blocks of OSCC tissues of patients treated with neck dissection were collected. Photomicrographs of H-&E-stained sections were subjected to the image analysis by ImageJ and Python programming to calculate nFD. The association of categorical variables with nFD was studied using cross-tabulation procedure and the Fisher exact test. Receiver operating curve analysis was performed to find out cutoff value of nFD. A logistic regression model was developed to test the individual and combined predictive potential of grading and nFD for cLNM. Results: A significant difference between the mean nFD of healthy cells and malignant epithelial cells was observed (P = 0.01). nFD was not found to be an independent predictor of cLNM, although nFD and grading together demonstrated significant predictive potential (P = 0.004). Conclusion: nFD combined with grading can predict lymph node metastasis in OSCC. To the best of our knowledge, this is the first study of its kind.
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Affiliation(s)
- Juber Rahman
- Department of Oral Pathology and Microbiology, Institute of Dental Sciences, Siksha 'O' Anusandhan Deemed to be University, Bhubaneswar, Odisha, India
| | - Swagatika Panda
- Department of Oral Pathology and Microbiology, Institute of Dental Sciences, Siksha 'O' Anusandhan Deemed to be University, Bhubaneswar, Odisha, India
| | - Santisudha Panigrahi
- Department of Computer Science and Engineering, Institute of Technical Education and Research, Siksha 'O' Anusandhan Deemed to be University, Bhubaneswar, Odisha, India
| | - Neeta Mohanty
- Department of Oral Pathology and Microbiology, Institute of Dental Sciences, Siksha 'O' Anusandhan Deemed to be University, Bhubaneswar, Odisha, India
| | - Tripti Swarnkar
- Department of Computer Science and Engineering, Institute of Technical Education and Research, Siksha 'O' Anusandhan Deemed to be University, Bhubaneswar, Odisha, India
| | - Umashankar Mishra
- Department of Management, School of Commerce and Management, Central university of Rajasthan, Ajmer, India
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Panigrahi S, Bhuyan R, Kumar K, Nayak J, Swarnkar T. Multistage classification of oral histopathological images using improved residual network. Math Biosci Eng 2022; 19:1909-1925. [PMID: 35135235 DOI: 10.3934/mbe.2022090] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Oral cancer is a prevalent disease happening in the head and neck region. Due to the high occurrence rate and serious consequences of oral cancer, an accurate diagnosis of malignant oral tumors is a major priority. Thus, early diagnosis is very effective to give the patient a prompt response to treatment. The most efficient way for diagnosing oral cancer is from histopathological imaging, which provides a detailed view of inside cells. Accurate and automatic classification of oral histopathological images remains a difficult task due to the complex nature of cell images, staining methods, and imaging conditions. The use of deep learning in imaging techniques and computational diagnostics can assist doctors and physicians in automatically analysing Oral Squamous Cell Carcinoma biopsy images in a timely and efficient manner. Thus, it reduces the operational workload of the pathologist and enhance patient management. Training deeper neural networks takes considerable time and requires a lot of computing resources, due to the complexity of the network and the gradient diffusion problem. With this motivation and inspired by ResNet's significant successes to handle the gradient diffusion problem, in this study we suggest the novel improved ResNet-based model for the automated multistage classification of oral histopathology images. Three prospective candidate model blocks are presented, analyzed, and the best candidate model is chosen as the optimal one which can efficiently classify the oral lesions into well-differentiated, moderately-differentiated and poorly-differentiated in significantly reduced time, with 97.59% accuracy.
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Affiliation(s)
- Santisudha Panigrahi
- Department of Computer Science and Engineering, SOA Deemed to be University Bhubaneswar, Odisha-751030, India
| | - Ruchi Bhuyan
- Oral Pathology and Microbiology, IMS, SUM Hospital, SOA Deemed to be University Bhubaneswar, Odisha-751030, India
| | - Kundan Kumar
- Department of Electronics and Communication Engineering, SOA Deemed to be University Bhubaneswar, Odisha-751030, India
| | - Janmenjoy Nayak
- Dept. of CSE, Aditya Institute of Technology and Management, Andhra Pradesh-532201, India
| | - Tripti Swarnkar
- Department of Computer Application, SOA Deemed to be University Bhubaneswar, Odisha-751030, India
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Mahapatra S, Bhuyan R, Das J, Swarnkar T. Integrated multiplex network based approach for hub gene identification in oral cancer. Heliyon 2021; 7:e07418. [PMID: 34258466 PMCID: PMC8258848 DOI: 10.1016/j.heliyon.2021.e07418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 01/27/2021] [Accepted: 06/23/2021] [Indexed: 02/01/2023] Open
Abstract
Background: The incidence of Oral Cancer (OC) is high in Asian countries, which goes undetected at its early stage. The study of genetics, especially genetic networks holds great promise in this endeavor. Hub genes in a genetic network are prominent in regulating the whole network structure of genes. Thus identification of such genes related to specific cancer types can help in reducing the gap in OC prognosis. Methods: Traditional study of network biology is unable to decipher the inter-dependencies within and across diverse biological networks. Multiplex network provides a powerful representation of such systems and encodes much richer information than isolated networks. In this work, we focused on the entire multiplex structure of the genetic network integrating the gene expression profile and DNA methylation profile for OC. Further, hub genes were identified by considering their connectivity in the multiplex structure and the respective protein-protein interaction (PPI) network as well. Results: 46 hub genes were inferred in our approach with a high prediction accuracy (96%), outstanding Matthews coefficient correlation value (93%) and significant biological implications. Among them, genes PIK3CG, PIK3R5, MYH7, CDC20 and CCL4 were differentially expressed and predominantly enriched in molecular cascades specific to OC. Conclusions: The identified hub genes in this work carry ontological signatures specific to cancer, which may further facilitate improved understanding of the tumorigenesis process and the underlying molecular events. Result indicates the effectiveness of our integrated multiplex network approach for hub gene identification. This work puts an innovative research route for multi-omics biological data analysis.
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Affiliation(s)
- S. Mahapatra
- Department of Computer Application, Siksha O Anusandhan Deemed to be University, Bhubaneswar, India
| | - R. Bhuyan
- Department of Oral Pathology & Microbiology, Siksha O Anusandhan Deemed to be University, Bhubaneswar, India
| | - J. Das
- Centre for Genomics & Biomedical Informatics, Siksha O Anusandhan Deemed to be University, Bhubaneswar, India
| | - T. Swarnkar
- Department of Computer Application, Siksha O Anusandhan Deemed to be University, Bhubaneswar, India
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Panigrahi S, Das J, Swarnkar T. Capsule network based analysis of histopathological images of oral squamous cell carcinoma. Journal of King Saud University - Computer and Information Sciences 2020. [DOI: 10.1016/j.jksuci.2020.11.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Nayak SK, Rout PK, Jagadev AK, Swarnkar T. Elitism based Multi-Objective Differential Evolution for feature selection: A filter approach with an efficient redundancy measure. Journal of King Saud University - Computer and Information Sciences 2020. [DOI: 10.1016/j.jksuci.2017.08.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Kumari B, Swarnkar T. Importance of Data Standardization Methods on Stock Indices Prediction Accuracy. Advances in Intelligent Systems and Computing 2020. [DOI: 10.1007/978-981-15-1081-6_26] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Pattanaik P, Swarnkar T. Comparative Analysis of Morphological Techniques for Malaria Detection. International Journal of Healthcare Information Systems and Informatics 2018. [DOI: 10.4018/ijhisi.2018100104] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The genus Plasmodium parasite causes malaria infection. Fast detection and accurate diagnosis of infected and non-infected malaria erythrocytes from microscopic blood smear images open the door to effective assistance and patient-specific treatment. This article presents a comparative experimental analysis of visual detection of infected erythrocytes malaria parasites via the most efficient morphological techniques from gold standard blood smear images. In this article, twelve different widely-used morphological algorithms are evaluated followed by a random forest classifier for detecting infected erythrocytes based on their performance vis-a-vis microscopic blood smear images. Accurate detection of infected malaria erythrocytes is done using the two ranges of blood smear image datasets with varying malaria parasite density. Finally, compared to 11 morphological techniques in terms of accuracy, sensitivity, and specificity, the qualitative assessment of experimental results unveil that the Histogram method offers more meaningful and impactful findings.
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Affiliation(s)
- P.A Pattanaik
- Department of Computer Science & Engineering, Siksha 'O' Anusandhan University, Bhubaneswar, India
| | - Tripti Swarnkar
- Department of Computer Science & Engineering, Siksha 'O' Anusandhan University, Bhubaneswar, India
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