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3. Secure and Privacy-Preserving Automated Machine Learning Operations into End-to-End Integrated IoT-Edge-Artificial Intelligence-Blockchain Monitoring System for Diabetes Mellitus Prediction

Author

Hennebelle, Alain, Ismail, Leila, Materwala, Huned, Kaabi, Juma Al, Ranjan, Priya, and Janardhanan, Rajiv

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Diabetes Mellitus, one of the leading causes of death worldwide, has no cure to date and can lead to severe health complications, such as retinopathy, limb amputation, cardiovascular diseases, and neuronal disease, if left untreated. Consequently, it becomes crucial to take precautionary measures to avoid/predict the occurrence of diabetes. Machine learning approaches have been proposed and evaluated in the literature for diabetes prediction. This paper proposes an IoT-edge-Artificial Intelligence (AI)-blockchain system for diabetes prediction based on risk factors. The proposed system is underpinned by the blockchain to obtain a cohesive view of the risk factors data from patients across different hospitals and to ensure security and privacy of the user's data. Furthermore, we provide a comparative analysis of different medical sensors, devices, and methods to measure and collect the risk factors values in the system. Numerical experiments and comparative analysis were carried out between our proposed system, using the most accurate random forest (RF) model, and the two most used state-of-the-art machine learning approaches, Logistic Regression (LR) and Support Vector Machine (SVM), using three real-life diabetes datasets. The results show that the proposed system using RF predicts diabetes with 4.57% more accuracy on average compared to LR and SVM, with 2.87 times more execution time. Data balancing without feature selection does not show significant improvement. The performance is improved by 1.14% and 0.02% after feature selection for PIMA Indian and Sylhet datasets respectively, while it reduces by 0.89% for MIMIC III.

Published
2022

9. Binary Classification of Mammograms Using Horizontal Visibility Graph

Author

Ghosh, Anirban, Ranjan, Priya, Chilamkurthy, Naga Srinivasarao, Gulati, Richa, Janardhanan, Rajiv, Ramakant, Pooja, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Kulkarni, Anand J., editor, Mirjalili, Seyedali, editor, and Udgata, Siba Kumar, editor

Published
2023
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10. Earth Mover’s Distance-Based Automated Disease Tagging of Indian ECGs

Author

Basha, Burhan, Nandi, Dhruva, Kaur, Karuna Nidhi, Arambam, Priyadarshini, Gupta, Shikhar, Segan, Mehak, Ranjan, Priya, Kaul, Upendra, Janardhanan, Rajiv, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Deva Sarma, Hiren Kumar, editor, Piuri, Vincenzo, editor, and Pujari, Arun Kumar, editor

Published
2023
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11. EMD-Based Binary Classification of Mammograms

Author

Ghosh, Anirban, Ramakant, Pooja, Ranjan, Priya, Deshpande, Anuj, Janardhanan, Rajiv, Tavares, João Manuel R. S., Series Editor, Jorge, Renato Natal, Series Editor, Frangi, Alejandro, Editorial Board Member, BAJAJ, CHANDRAJIT, Editorial Board Member, Onate, Eugenio, Editorial Board Member, Perales, Francisco José, Editorial Board Member, Holzapfel, Gerhard A., Editorial Board Member, Vilas-Boas, João, Editorial Board Member, Weiss, Jeffrey, Editorial Board Member, Middleton, John, Editorial Board Member, Garcia Aznar, Jose Manuel, Editorial Board Member, Nithiarasu, Perumal, Editorial Board Member, Tamma, Kumar K., Editorial Board Member, Cohen, Laurent, Editorial Board Member, Doblare, Manuel, Editorial Board Member, Prendergast, Patrick J., Editorial Board Member, Löhner, Rainald, Editorial Board Member, Kamm, Roger, Editorial Board Member, Li, Shuo, Editorial Board Member, Hughes, Thomas J.R., Editorial Board Member, Zhang, Yongjie, Editorial Board Member, Gupta, Mousumi, editor, Ghatak, Sujata, editor, Gupta, Amlan, editor, and Mukherjee, Abir Lal, editor

Published
2023
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15. Knowledge and Awareness of Breast Cancer and Breast Self-Examination among College-Going Female Students in Delhi-NCR: A Cross Sectional Study

Author

Mukherjee Das, Ankan, Shrivastav, Kumar Dron, Taneja, Neha, Awasthi, Aanchal Anant, Rashid, Shazia, Gogia, Ajay, and Janardhanan, Rajiv

Abstract

Purpose: Breast cancer (BC) presents a major public health challenge world-over including India. While several risk-factors, early signs and symptoms of BC are known, the knowledge and awareness of this disease remains poor among the population. The present study aimed to determine the extent of knowledge and awareness of BC, its risk factors, early signs and symptoms and breast self-examination (BSE) practice as an early detection method among Indian college-going female students. Design/methodology/approach: The authors conducted a cross-sectional survey at a University in Delhi-NCR. Data on socio-demographic, knowledge and awareness of BC including BSE was collected using a pretested questionnaire. Chi-square test and logistic regression analysis was performed. All tests were two-sided and significance was set at p < 0.05. Findings: A total of 866 female students participated in the study with mean age of 22.32 (±0.146) years having mean body mass index (BMI) of 21.22 (±3.52). As high as 82.1% of the participants had heard of BC but while 74.8% thought early detection is possible, 70.7% believed BC cannot be prevented. Gene mutations (60.2%) were identified as a significant risk factor, while breast pain (61.4%) was commonly recognized as a sign of BC. Only 29.8% of students ever performed BSE. Increased odds of performing BSE (OR = 3.4) was found among students who recognized gene mutations as an important BC risk factor. Research limitations/implications: Knowledge and awareness of BC including BSE among female college students were found to be below average. It is suggested that there is an urgent need for increasing BC awareness among young girls through workshops and mobile-health interventions. Practical implications: This study provides new information on the level of knowledge and awareness of BC risk factors, sign and symptoms and self-examination practice among young college girls. Moreover, this study advocates the need for design and implementation of a sustainable digital health model for active population BC screening, which is not being done currently. Social implications: BC is a highly aggressive disease, which is now one of the leading causes of morbidity and mortality in India and world over. Although the knowledge of BC risk factors and its signs and symptoms have increased, the awareness of these elements among the general population at large is low and/or missing, especially in India. Furthermore, as a consequence of unorganized screening programs in the country, majority of women are presenting young with locally advanced disease. Understanding the existing level of knowledge and educating school, college and University students of the pertinent factors and screening practices such as BSE could drastically help in improving the self-screening and/or clinical examination rates. This could potentially lead to early detection and improved prognosis, thus ameliorating disease burden. Originality/value: This study is one of the few studies conducted in India among young female college students belonging to non-medical backgrounds, delineating the level of knowledge and awareness of BC risk factors and signs and symptoms along with practice of early detection method such as BSE. The study has a considerable sample size and provides valuable evidence for a need to implement programs incorporating digital health models for accelerating awareness and screening of young girls in both rural and urban settings.

Published
2022
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16. EMD and Horizontal Visibility Graph Based Disease Tagging for Covid-Positive Chest Radiographs

Author

Chavan, Niranjan, Ranjan, Priya, Shrivastav, Kumar Dron, Janardhanan, Rajiv, Kasabov, Nikola, Series Editor, Amari, Shun-ichi, Editorial Board Member, Avesani, Paolo, Editorial Board Member, Benuskova, Lubica, Editorial Board Member, Brown, Chris M., Editorial Board Member, Duro, Richard J, Editorial Board Member, Georgieva, Petia, Editorial Board Member, Hou, Zeng-Guang, Editorial Board Member, Indiveri, Giacomo, Editorial Board Member, King, Irwin, Editorial Board Member, Kozma, Robert, Editorial Board Member, König, Andreas, Editorial Board Member, Mandic, Danilo, Editorial Board Member, Masulli, Francesco, Editorial Board Member, Thivierge, JeanPhilippe, Editorial Board Member, Villa, Allessandro E.P, Editorial Board Member, Adibi, Sasan, editor, Rajabifard, Abbas, editor, Shariful Islam, Sheikh Mohammed, editor, and Ahmadvand, Alireza, editor

Published
2022
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17. Specular Reflection Removal in Cervigrams

Author

Agarwal, Ayush, Ali, Faraaz, Kopparthi, Aditya, Ranjan, Priya, Shrivastav, Kumar Dron, Janardhanan, Rajiv, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Sarma, Hiren Kumar Deva, editor, Balas, Valentina Emilia, editor, Bhuyan, Bhaskar, editor, and Dutta, Nitul, editor

Published
2022
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18. Kloman Metre: An EMD-Based Tool for Triaging Diseases Leading to Lung Infections Including COVID-19

Author

Chavan, Niranjan, Ranjan, Priya, Kumar, Uday, Shrivastav, Kumar Dron, Sarma, Hiren Kumar Deva, Janardhanan, Rajiv, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Sarma, Hiren Kumar Deva, editor, Balas, Valentina Emilia, editor, Bhuyan, Bhaskar, editor, and Dutta, Nitul, editor

Published
2022
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24. HRIDAI: A Tale of Two Categories of ECGs

Author

Ranjan, Priya, Shrivastav, Kumar Dron, Vadlamani, Satya, Janardhanan, Rajiv, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Thampi, Sabu M., editor, Krishnan, Sri, editor, Hegde, Rajesh M., editor, Ciuonzo, Domenico, editor, Hanne, Thomas, editor, and Kannan R., Jagadeesh, editor

Published
2021
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25. Pneumonia Identification in Chest X-Ray Images Using EMD

Author

Khatri, Archit, Jain, Rishabh, Vashista, Hariom, Mittal, Nityam, Ranjan, Priya, Janardhanan, Rajiv, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Sarma, Hiren Kumar Deva, editor, Bhuyan, Bhaskar, editor, Borah, Samarjeet, editor, and Dutta, Nitul, editor

Published
2020
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26. Earth Mover’s Distance-Based Automated Geometric Visualization/Classification of Electrocardiogram Signals

Author

Shrivastav, Kumar Dron, Arambam, Priyadarshini, Das, Ankan Mukherjee, Saeed, Shazina, Kaul, Upendra, Ranjan, Priya, Janardhanan, Rajiv, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Sarma, Hiren Kumar Deva, editor, Bhuyan, Bhaskar, editor, Borah, Samarjeet, editor, and Dutta, Nitul, editor

Published
2020
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30. Classification of Colposcopic Cervigrams Using EMD in R

Author

Shrivastav, Kumar Dron, Mukherjee Das, Ankan, Singh, Harpreet, Ranjan, Priya, Janardhanan, Rajiv, Barbosa, Simone Diniz Junqueira, Series Editor, Filipe, Joaquim, Series Editor, Kotenko, Igor, Series Editor, Sivalingam, Krishna M., Series Editor, Washio, Takashi, Series Editor, Yuan, Junsong, Series Editor, Zhou, Lizhu, Series Editor, Ghosh, Ashish, Series Editor, Thampi, Sabu M., editor, Marques, Oge, editor, Krishnan, Sri, editor, Li, Kuan-Ching, editor, Ciuonzo, Domenico, editor, and Kolekar, Maheshkumar H., editor

Published
2019
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41. Knowledge, attitude, and practice towards breast cancer and its screening among women in India: A systematic review

Author

Pal, Akanksha, Taneja, Neha, Malhotra, Neha, Shankar, Rajashree, Chawla, Bhavika, Awasthi, Aanchal, and Janardhanan, Rajiv

Subjects
Women -- Health aspects, Cancer -- Diagnosis, Health literacy -- Surveys, Health attitudes -- Surveys, Breast cancer -- Surveys, Health
Abstract

Byline: Akanksha. Pal, Neha. Taneja, Neha. Malhotra, Rajashree. Shankar, Bhavika. Chawla, Aanchal. Awasthi, Rajiv. Janardhanan Background: Breast cancer is the most common cancer among women in India causing increased mortality. [...]

Published
2021

44. Secure and Privacy-Preserving Automated End-to-End Integrated IoT-Edge-Artificial Intelligence-Blockchain Monitoring System for Diabetes Mellitus Prediction

Author

Ismail, Leila, Hennebelle, Alain, Materwala, Huned, Kaabi, Juma Al, Ranjan, Priya, and Janardhanan, Rajiv

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Machine Learning (cs.LG)
Abstract

Diabetes Mellitus, one of the leading causes of death worldwide, has no cure till date and can lead to severe health complications, such as retinopathy, limb amputation, cardiovascular diseases, and neuronal disease, if left untreated. Consequently, it becomes crucial to take precautionary measures to avoid/predict the occurrence of diabetes. Machine learning approaches have been proposed and evaluated in the literature for diabetes prediction. This paper proposes an IoT-edge-Artificial Intelligence (AI)-blockchain system for diabetes prediction based on risk factors. The proposed system is underpinned by the blockchain to obtain a cohesive view of the risk factors data from patients across different hospitals and to ensure security and privacy of the user data. Furthermore, we provide a comparative analysis of different medical sensors, devices, and methods to measure and collect the risk factors values in the system. Numerical experiments and comparative analysis were carried out between our proposed system, using the most accurate random forest (RF) model, and the two most used state-of-the-art machine learning approaches, Logistic Regression (LR) and Support Vector Machine (SVM), using three real-life diabetes datasets. The results show that the proposed system using RF predicts diabetes with 4.57% more accuracy on average compared to LR and SVM, with 2.87 times more execution time. Data balancing without feature selection does not show significant improvement. The performance is improved by 1.14% and 0.02% after feature selection for PIMA Indian and Sylhet datasets respectively, while it reduces by 0.89% for MIMIC III.

Published
2022

50. Earth Mover’s Distance-Based Tool for Rapid Screening of Cervical Cancer Using Cervigrams

Author

Shrivastav, Kumar Dron, Shrivastav, Kumar Dron, Arambam, Priyadarshini, Batra, Shelly, Bhatia, Vandana, Singh, Harpreet, Jaggi, Vinita Kumar, Ranjan, Priya, Abed, Eyad H., Janardhanan, Rajiv, Shrivastav, Kumar Dron, Shrivastav, Kumar Dron, Arambam, Priyadarshini, Batra, Shelly, Bhatia, Vandana, Singh, Harpreet, Jaggi, Vinita Kumar, Ranjan, Priya, Abed, Eyad H., and Janardhanan, Rajiv

Abstract

Cervical cancer is a major public health challenge that can be cured with early diagnosis and timely treatment. This challenge formed the rationale behind our design and development of an intelligent and robust image analysis and diagnostic tool/scale, namely “OM—The OncoMeter”, for which we used R (version-3.6.3) and Linux (Ubuntu-20.04) to tag and triage patients in order of their disease severity. The socio-demographic profiles and cervigrams of 398 patients evaluated at OPDs of Batra Hospital & Medical Research Centre, New Delhi, India, and Delhi State Cancer Institute (East), New Delhi, India, were acquired during the course of this study. Tested on 398 India-specific women’s cervigrams, the scale yielded significant achievements, with 80.15% accuracy, a sensitivity of 84.79%, and a specificity of 66.66%. The statistical analysis of sociodemographic profiles showed significant associations of age, education, annual income, occupation, and menstrual health with the health of the cervix, where a p-value less than (<) 0.05 was considered statistically significant. The deployment of cervical cancer screening tools such as “OM—The OncoMeter” in live clinical settings of resource-limited healthcare infrastructure will facilitate early diagnosis in a non-invasive manner, leading to a timely clinical intervention for infected patients upon detection even during primary healthcare (PHC).

Published
2022

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