ARCHIVES

[1]. K. Kalaiselvi, M. Deepika, Machine Learning for Healthcare Diagnostics, in: Machine Learning with Health Care Perspective,
Springer, Cham, 2020, pp. 91–105, https://doi.org/10.1007/978-3-030-40850-3
[2]. J.W. Baek, K. Chung, Context deep neural network model for predicting depression risk using multiple regression, IEEE Access 21 (8)(2020 Jan)18171–18181, https://doi.org/10.1109/ACCESS.2020.2968393.
[3]. Bai, Qiong, et al. "Machine learning to predict end stage kidney disease in chronic kidney disease."Scientific reports 12.1 (2022): 1-8.
[4]. Ifraz, G. M., Rashid, M. H., Tazin, T., Bourouis, S., & Khan, M. M. (2021). Comparative Analysis for Prediction of Kidney Disease
Using Intelligent Machine Learning Methods.Computational and Mathematical Methods in Medicine, 2021.
[5]. YongFeng Wang,(2020). “Comparison Study of Radiomics and Deep-Learning Based Methods for Thyroid Nodules Classification
using Ultrasound Images” published on IEEEAccess.
[6]. Priyanka Sonar, Prof. K. JayaMalini,” DIABETES PREDICTION USING DIFFERENT MACHINE LEARNING APPROACHES”,
2019 IEEE ,3rd International Conference on Computing Methodologies and Communication (ICCMC)
[7]. DeeptiSisodia, Dilip Singh Sisodia, Prediction of Diabetes using Classification Algorithms, International Conference on
Computational Intelligence and Data Science (ICCIDS 2018), Procedia Computer Science 132 (2018) 1578–1585.pp 1578-1585.
[8]. ArchanaSingh ,Rakesh Kumar, “Heart Disease Prediction Using Machine Learning Algorithms”, 2020 IEEE, International
Conference on Electrical and Electronics Engineering (ICE3)
[9]. Qin, Jiongming& Chen, Lin & Liu, Yuhua& Liu, Chuanjun&Feng, Changhao& Chen, Bin.(2019). A Machine Learning Methodology
for Diagnosing Chronic Kidney Disease. IEEE Access. PP. 1-1. 10.1109/ACCESS.2019.2963053.
[10]. Dilip Kumar Choubey, Sanchita Paul, GA_MLP NN: A Hybrid Intelligent System for Diabetes Disease Diagnosis, I.J. Intelligent
Systems and Applications, 2016, 1, 49-59
[11]. VijiyaKumar, K., Lavanya, B., Nirmala, I., & Caroline, S. S. (2019). Random Forest Algorithm for the Prediction of Diabetes. 2019
IEEE International Conference on System, Computation, Automation and Networking (ICSCAN). doi:10.1109/icscan.2019.8878802.
[12]. G. Kaur and A. Sharma, "Predict chronic kidney disease using data mining algorithms in hadoop," 2017 International Conference on
Inventive Computing and Informatics (ICICI), pp. 973-979.
[13]. Mohan, S., Thirumalai, C., Srivastava, G.: Effective heart disease prediction using hybrid machine learning techniques. IEEE Access
2019.
[14].Maurya, A., Wable, R., Shinde, R., John, S.B., Jadhav, R., & Dakshayani, R. (2019). Chronic Kidney Disease Prediction and
Recommendation of Suitable Diet Plan by using Machine Learning. 2019 International Conference on Nascent Technologies in
Engineering (ICNTE), 1-4.