NANDAKUMAR P
PUBLICATIONS
Ø Nandakumar Pandiyan and Subhashini Narayan, “Comparative Analysis of Cardiac Disease Classification Using a Deep Learning Model Embedded with a Bio-Inspired Algorithm” International Journal of Advanced Computer Science and Applications(IJACSA), 16(2), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160296.
Ø P. Nandakumar and R. Subhashini, "Heart Disease Prediction Using Convolutional Neural Network with Elephant Herding Optimization," Computer Systems Science and Engineering, vol. 48, no.1, pp. 57–75, 2024. https://doi.org/10.32604/csse.2023.042294.
Ø Nandakumar, P., Narayan, S. (2023). Cardial Disease Prediction in
Multi-variant Systems Using MT-MrSBC Model. In: Deva Sarma, H.K., Piuri,
V., Pujari, A.K. (eds) Machine Learning in Information and Communication
Technology . Lecture Notes in Networks and Systems, vol 498. Springer,
Singapore. https://doi.org/10.1007/978-981-19-5090-2_2.
Ø Nandakumar P, Subhashini Narayan, “A Survey on Deep Learning
Models Embed Bio-Inspired Algorithms in Cardiac Disease Classification”,
The Open Biomedical Engineering Journal, Volume 16, 2022, http://dx.doi.org/10.2174/18741207-v16-e221227-2022-HT27-3589-14.
Ø Nandakumar P, Subhashini Narayan, “Cardiac disease detection
using cuckoo search enabled deep belief network”, Intelligent Systems with
Applications, Volume 16, 2022, 200131, ISSN 2667-3053, https://doi.org/10.1016/j.iswa.2022.200131.
Ø Nandakumar Pandiyan, Subhashini Narayan, "Prediction of
Cardiac Disease using Kernel Extreme Learning Machine Model,"
International Journal of Engineering Trends and Technology, vol. 70, no. 11,
pp. 364-377, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I11P238.
Ø K. Lokeshwaran, A. Rajesh, P. Nandakumar, "Flagged Approach to Detect Broken Links in Linked Open Data", International
Organization of Scientific Research (IOSR), ISSN: 2250-3021, Journal Name:
2250-3021, Volume No.: 8, Issue No.: 11, 2018.
Ø Nandakumar P, Mohamed Yousuff A R, Abdul Naseer M, Fathima Begum M,
Balaji T, “Machine Learning and Data
Mining Scheme in Cloud Using Distributed Snapshot Algorithm”, International
Journal of Current Engineering and Scientific Research (IJCESR), ISSN (PRINT):
2393-8374, (ONLINE): 2394-0697, VOLUME-5, ISSUE-3, 2018.
Ø Balaji T, Mohamed Yousuff A R, Abdul Naseer M, Nandakumar P, Fathima
Begum M, “Machine Learning on Event
Streams in A Distributed Environment by A Streamlearner”, International
Journal of Current Engineering and Scientific Research (IJCESR), ISSN (PRINT):
2393-8374, (ONLINE): 2394-0697, VOLUME-5, ISSUE-3, 2018.
Ø M.Fathima Begum, M.Abdul Naseer, C.Kotteeswaran, T.Balaji,
P.Nandakumar, “Location Of Ddos Botnet
Attacks For Cyber Security”, International Journal Of Current Engineering
And Scientific Research (IJCESR), ISSN (PRINT): 2393-8374, (ONLINE): 2394-0697,
VOLUME-5, ISSUE-3, 2018.
Ø M. Fathima Begum, P. Nanda Kumar, T. Balaji, A.R. Mohamed Yousuff, M.
Abdul Naseer, “Clustering Web Documents
to Bootstrap the Discovery of Web Services”, IJSART - Volume3, Issue 2,
FEBRUARY 2017.
Ø T. Balaji, P. Nanda Kumar, M. Fathima Begum, M. Abdul Naseer, S.
Abdul Kani,” An Efficient Data Gathering
and Aggregation for Multiple Applications in Wireless Sensor Networks”,
IJIRST –International Journal for Innovative Research in Science &
Technology, Volume 2, Issue 11, APRIL 2016.
CONFERENCES
- Presented
a paper titled "A Review on Bio-Inspired Population-Based Algorithms"
in the Virtual Conference on Ubiquitous AI and Machine Learning organized by
Institute for Engineering Research and Publication (IFERP) held on 16th &
17th October 2020.
- Participated and presented a paper titled, “Cardial
Disease Prediction In Multi-Variant Systems using MT-MrSBC Model” at the
First International Conference on Information and Communication Technology
(ICICT) 2021, 23-24 DECEMBER 2021, Organized by the Department of Information
Technology, SMIT.
Internal Full-Time Ph.D. at Vellore Institute of Technolgy, Vellore Main Campus - Publication Link
Article 1:
Abstract:
Cardiac disease is the most infected disease in the world nowadays for all ages of people. An emergency need arises to predict cardiac disease accurately in a short time. In this article, the hamming distance feature selection method is proposed for the data preprocessing and data cleaning process in different cardiac disease datasets. Deep learning model such as deep belief networks is used with a cuckoo search bio-inspired algorithm for finding the accurate prediction of cardiac disease. The results demonstrate that deep belief networks with the cuckoo search algorithm have achieved good performance with an accuracy of 89.2% from Cleveland, 89.5% from South Africa, and 89.7% from Z-Alizadeh Sani, 90.2% from Framingham, and 91.2% from Statlog cardiac disease datasets.
Article 2:
Abstract:
Cardiac disease is now a major cause of death for people affected by COVID-19. For the past five years, the death rate of people affected by cardiac disease has increased a lot. In recent years, many deep learning models have provided prominent results for predicting it from different UCI heart disease data and other ECG data. Cardiac disease can be predicted from medical diagnosis and electrocardiogram data. Even though many types of detection for cardiac disease are available, ECG plays a major role in identifying it accurately. However, still, there is some gap in identifying the correct data, cleaning the unwanted features with popular methods, and optimizing it for better accuracy. In this paper, we propose a deep learning model, such as an Extreme Learning Machine (ELM), for predicting cardiac disease from the benchmark dataset, such as the MIT-BIH Arrhythmia dataset available in the PhysioNet database. The Principal Component Analysis is used to extract and identify the best features. Transfer learning is additionally used with kernel ELM for the improvement of the classification performance of ELM. Finally, the proposed Extreme Learning Machine model classifies cardiac disease with a promising result of 98.50% accuracy. In future research, it can be predicted in various datasets for performance improvement by selecting all other ensemble models.
Article 3:
Abstract:
Heart disease is a major cause of death for many people in the world. Each year the death rate of people affected with heart disease increases a lot. Machine learning models have been widely used for the prediction of heart disease from the different University of California Irvine (UCI) Machine Learning Repositories. But, due to certain data, it predicts less accurately, whereas, for large data, its sub-model deep learning is used. Our literature work has identified that only traditional methods are used for the prediction of heart disease. It will produce less accuracy. To produce more efficacy, Euclidean Distance was used in this work for data pre-processing that will clean the unwanted data, and metaheuristics bio-inspired algorithms such as elephant herding optimization (EHO) are utilized for feature selection. Then, this article proposes deep learning models such as convolutional neural network (CNN) and Inception-ResNet-v2 model for the prediction of heart disease from the benchmark dataset such as the UCI Cleveland heart dataset. Finally, the proposed hybrid model utilizes a convolutional neural network with an Inception-ResNet-v2 in the third layer of the architecture that classifies heart disease with the promising result of 98.77%, accuracy for the Cleveland dataset which outperforms all the other state-of-the-art methods. In future work, this model can be used to predict other diseases such as cancer, brain tumors, and COVID-19 in available datasets for the betterment of human lives.
Article 4:
Abstract:
Cardiac disease classification is a crucial task in healthcare aimed at early diagnosis and prevention of cardiovascular complications. Traditional methods such as machine learning models often face challenges in handling high-dimensional and noisy datasets, as well as in optimizing model performance. In this study, we propose and compare a novel approach for heart disease prediction using deep learning models embedded in bioinspired algorithms. The integration of deep learning techniques allows for automatic feature learning and complex pattern recognition from raw data, while bioinspired algorithms provide optimization capabilities for enhancing model accuracy and generalization. Specifically, the cuckoo search algorithm and elephant herding optimization algorithm are employed to optimize the architecture and hyperparameters of deep learning models, facilitating the exploration of diverse model configurations and parameter settings. This hybrid approach enables the development of highly effective predictive models by efficiently leveraging the complementary strengths of deep learning and bioinspired optimization. Experimental results on benchmark heart disease datasets demonstrate the superior performance of the proposed method compared to conventional approaches, achieving higher accuracy and robustness in predicting heart disease risk. The proposed framework holds significant promise for advancing the state-of-the-art in heart disease prediction and facilitating personalized healthcare interventions for at-risk individuals.
Survey Article :
Abstract:
Deep learning is a sub-field of machine learning that emerged as a noticeable model in the world, specifically for the disease classification field. This work aims to review the state-of-the-art deep learning models in Cardiac Disease prediction by examining several research papers. In this study, popular datasets listed and analyzed in the prediction process of cardiac disease with their performance using various deep learning techniques are presented. This review emphasizes the latest advancement in the six deep learning models, namely, deep neural networks, convolutional neural networks, recurrent neural networks, extreme learning machines, deep belief networks, and transfer learning with its applications. The important features of cardiac disease used by five different countries have been listed to guide researchers in analyzing it for future purposes. Freshly, deep learning models have yielded an extended performance in cardiac disease detection that shows its rapid growth. Specifically, deep learning effectiveness concerted with the bio-inspired algorithms is reviewed. This paper also presents what major applications of deep learning techniques have been grasped in the past decade.
Conference Article :
Abstract:
Heart disease has been a major threat that costs human lives. There are many reasons behind heart disease including smoking, heredity, and diabetes. Day to day, people face various common symptoms of heart disease which are unconsidered in a lethargic manner. This leads to serious and life-threatening complications. To predict these diseases in prior, several methods are existing that take in a certain number of parameters for prognosis. The system proposed here is an ensemble approach that combines the idea of the MT-MrSBC algorithm along with bagging and boosting. The algorithm mentioned here overcomes the issues faced by other algorithms in handling the multi-variant environment. The algorithm deploys iterative techniques indulging bagging and boosting concepts that enhance the system. The system trained is thus capable of predicting the disease of the patient. This helps in taking precautionary measures by the patient which are life-saving.
NANDAKUMAR P
Teaching cum Research Assistant (TRA), (19PHD0504/16828),
School of Information Technology and Engineering,
Vellore Institute of Technology, Vellore-632014
Email ID: pndlnandu@gmail.com / nandakumarpandiyan3@gmail.com / nandakumar.p2019@vitstudent.ac.in
Contact No: 9894388230/8667226163
Scopus ID: 57930513000
Web of Science ResearcherID: HII-5621-2022
Vellore Institute of Technology (VIT), India -
- Ranked among the top 601-700 Universities of the world and one among the top 3 Institutions in India (Shanghai ARWU Ranking 2022)
- the 9th best University, the 10th best research institution and the 12th best engineering institution in India (NIRF Ranking, Govt. of India 2022)
- Engineering and Technology subject areas of VIT are the 346th best in the World and the 9th best in India as per QS World University Rankings by Subject 2022
- NAAC Accreditation with A++ grade in the 4th cycle
- Ranked within the top 200 Universities in Asia (QS - Asia University Rankings 2022)
