Computer Science, Management Science and Operations Research
4
Scopus Publications
6
Scholar Citations
1
Scholar h-index
Scopus Publications
Analysis of chronic kidney disease using machine learning techniques Tella Pavani, D. Krishna Murali, K. Lakshmi Ajita, G. N. S. Lakshmi, G. Hitha Pooja, G. Akshitha, Ch. Tejaswini Recent Trends in VLSI and Semiconductor Packaging, 2025 Chronic kidney disease (CKD) is a serious global health concern that has a high rate of morbidity and mortality and can cause other disorders. It’s a serious condition that can last a lifetime and is brought on by either diminished kidney function or kidney cancer. The early stages of CKD are characterized by a lack of evident symptoms, which might make patients unaware of the disease. It is possible to stop or slow the course of this chronic illness until it reaches a point where a patient’s only options for survival are dialysis or surgery. Patients with early-stage CKD can benefit from early therapy, which can also slow the disease’s progression. Therefore, having an effective model is crucial for CKD early diagnosis. Because machine learning models execute identification tasks quickly and accurately, they can help therapists accomplish this goal. Here, we recommend using machine learning to diagnose CKD. The CKD dataset, which has a significant amount of missing information, will be made available through the University of California, Irvine (UCI) machine learning repository. Using machine learning techniques such as support vector machines, decision trees, neural networks, and K-nearest neighbor for the detection of chronic kidney disease is the goal of this proposal. Feature engineering algorithms such as ANOVA, MRMR, and CHI2 algorithms are used to determine the features for predicting CKD.
Assessment of Classification Techniques for Heart Disease Prediction Lakshmi. G, P. Sujatha Proceedings of the International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering Iceconf 2023, 2023 Prediction of cardiac illness remains the most baffling errand in the ground of clinical disciplines. The present clinical calling has gone through a surprising progression to oblige people experiencing an assortment of ailments. One of the most essential aspects for clinical professionals is to diagnose coronary heart disease, especially if it is computer based with the goal of a quick diagnosis and a predictable outcome. Opportune screening of the presence of coronary illness can save a patient's life. Despite the fact that doctors have identified a number of triggers for heart attacks. The point of the study is distinguished between the use of Machine Learning Techniques for cardiovascular infections solicitation and presumption. In terms of clinical limits, this survey focused on datasets that incorporated formulated a model. Using Machine Learning Techniques, this framework evaluates such boundaries. According to the comparative examination of all other approaches, the Support Vector Machine technique has several advantages to be a credible manner of anticipating coronary illness.
Novel Two Level Classification (21-C) Model For Heart Disease Prediction Proceedings of the 17th Indiacom 2023 10th International Conference on Computing for Sustainable Global Development Indiacom 2023, 2023
RECENT SCHOLAR PUBLICATIONS
Optimal NAS-MoE: optimized NASNet and MoE model for lung adenocarcinoma classification with explainable AI GG Lakshmi, P Chinnasamy, P Nagaraj Biomedical Signal Processing and Control 113, 109128 , 2026 2026.0 Citations: 3
An Extensive Study on Heart Attack Prediction Using Different ML And DL Approaches Integrated with Genetic Algorithms GVR Lakshmi, CVMK Hari, V Rao Advances in Intelligent Systems, 75-107 , 2025 2025.0
An Approach for Lung Cancer Detection Using an Optimization-Enabled Squeeze-Inception V3 Model GG Lakshmi, P Nagaraj, P Chinnasamy International Conference on ICT for Sustainable Development, 277-292 , 2025 2025.0 Citations: 1
AI-Based Multi Detection and Classification Method for Lung Cancer and Pneumonia Using Deep Learning with VGG19 and YOLO V8 ILF on X-Ray Images K Nandhini, R Thilagavathy, G Lakshmi International Conference on Evolutionary Artificial Intelligence, 505-522 , 2024 2024.0
An efficient approach of heart disease diagnosis using modified principal component analysis (M-PCA) G Lakshmi, P Sujatha International Conference on Computational Sciences and Sustainable … , 2023 2023.0 Citations: 1
Early Detection and Classification of Heart Diseases by Employing IFCMML and 2L-C Model with I-GA Machine Learning Methods G Lakshmi, P Sujatha Indian Journal of Science and Technology 16 (15), 1107-1117 , 2023 2023.0
Novel Two Level Classification (21-C) Model For Heart Disease Prediction G Lakshmi, P Sujatha 2023 10th International Conference on Computing for Sustainable Global … , 2023 2023.0
Assessment of Classification Techniques for Heart Disease Prediction SP Lakshmi G 2023 International Conference on Artificial Intelligence and Knowledge … , 2023 2023.0 Citations: 1
Improved Survey of Heart Disease Diagnosis and Prediction Using Classification Techniques G Lakshmi, GV Sriramakrishnan Annals of the Romanian Society for Cell Biology 25 (5), 839-847 , 2021 2021.0
Quantum-Enhanced Lightweight Protocol for Post-Classical Security in IoT-Enabled Autonomous Vehicle Networks G Lakshmi, A Senthilkumar, HJ VL, R Kowsalya, S Nithyanandh
MOST CITED SCHOLAR PUBLICATIONS
Optimal NAS-MoE: optimized NASNet and MoE model for lung adenocarcinoma classification with explainable AI GG Lakshmi, P Chinnasamy, P Nagaraj Biomedical Signal Processing and Control 113, 109128 , 2026 2026.0 Citations: 3
An Approach for Lung Cancer Detection Using an Optimization-Enabled Squeeze-Inception V3 Model GG Lakshmi, P Nagaraj, P Chinnasamy International Conference on ICT for Sustainable Development, 277-292 , 2025 2025.0 Citations: 1
An efficient approach of heart disease diagnosis using modified principal component analysis (M-PCA) G Lakshmi, P Sujatha International Conference on Computational Sciences and Sustainable … , 2023 2023.0 Citations: 1
Assessment of Classification Techniques for Heart Disease Prediction SP Lakshmi G 2023 International Conference on Artificial Intelligence and Knowledge … , 2023 2023.0 Citations: 1
An Extensive Study on Heart Attack Prediction Using Different ML And DL Approaches Integrated with Genetic Algorithms GVR Lakshmi, CVMK Hari, V Rao Advances in Intelligent Systems, 75-107 , 2025 2025.0
AI-Based Multi Detection and Classification Method for Lung Cancer and Pneumonia Using Deep Learning with VGG19 and YOLO V8 ILF on X-Ray Images K Nandhini, R Thilagavathy, G Lakshmi International Conference on Evolutionary Artificial Intelligence, 505-522 , 2024 2024.0
Early Detection and Classification of Heart Diseases by Employing IFCMML and 2L-C Model with I-GA Machine Learning Methods G Lakshmi, P Sujatha Indian Journal of Science and Technology 16 (15), 1107-1117 , 2023 2023.0
Novel Two Level Classification (21-C) Model For Heart Disease Prediction G Lakshmi, P Sujatha 2023 10th International Conference on Computing for Sustainable Global … , 2023 2023.0
Improved Survey of Heart Disease Diagnosis and Prediction Using Classification Techniques G Lakshmi, GV Sriramakrishnan Annals of the Romanian Society for Cell Biology 25 (5), 839-847 , 2021 2021.0
Quantum-Enhanced Lightweight Protocol for Post-Classical Security in IoT-Enabled Autonomous Vehicle Networks G Lakshmi, A Senthilkumar, HJ VL, R Kowsalya, S Nithyanandh
