Diagnosing COVID-19 using artificial intelligence: a comprehensive review. - Varada Vivek Khanna; Krishnaraj Chadaga; Niranjana Sampathila; Srikanth Prabhu; Rajagopala Chadaga; Shashikiran Umakanth (Netw. Model. Anal. Health Informatics Bioinform., 2022)

Severity prediction in COVID-19 patients using clinical markers and explainable artificial intelligence: A stacked ensemble machine learning approach. - Krishnaraj Chadaga; Srikanth Prabhu; Niranjana Sampathila; Rajagopala Chadaga (Intell. Decis. Technol., 2023)

Multiple Explainable Approaches to Predict the Risk of Stroke Using Artificial Intelligence. - Susmita S.; Krishnaraj Chadaga; Niranjana Sampathila; Srikanth Prabhu; Rajagopala Chadaga; Swathi Katta S. (Inf., 2023)

An Explainable Decision Support Framework for Differential Diagnosis Between Mild COVID-19 and Other Similar Influenzas. - Krishnaraj Chadaga; Srikanth Prabhu; Niranjana Sampathila; Rajagopala Chadaga; Shashikiran Umakanth (IEEE Access, 2024)

Using Explainable Machine Learning Methods to Predict the Survivability Rate of Pediatric Respiratory Diseases. - Roshan Kumar; V. Srirama; Krishnaraj Chadaga; H. Muralikrishna; Niranjana Sampathila; Srikanth Prabhu; Rajagopala Chadaga (IEEE Access, 2024)

Demystifying multiple sclerosis diagnosis using interpretable and understandable artificial intelligence. - Krishnaraj Chadaga; Varada Vivek Khanna; Srikanth Prabhu; Niranjana Sampathila; Rajagopala Chadaga; Anisha Palkar (J. Intell. Syst., 2024)