This a project where Cardiovascular disease is predicted using an ensemble approach, the base classifiers used in the model are Naive Bayes (NB), Support Vector Machine (SVM), k-Nearest Neighbors (KNN) and Xtreme Gradient Boosting (XGB). I have published research articles on this topic and I made it public to help others in creating new machine learning frameworks. To know more about this project, visit the article links provided below.
https://ieeexplore.ieee.org/document/10430692
Citation
V. Chowdary B, C. Datta M and R. Senapati, "An Improved Cardiovascular Disease Prediction Model Using Ensembling of Diverse Machine Learning Classifiers," 2023 OITS International Conference on Information Technology (OCIT), Raipur, India, 2023, pp. 329-333, doi: 10.1109/OCIT59427.2023.10430692. keywords: {Heart;Support vector machines;Measurement;Robustness;Cardiovascular diseases;Task analysis;Standards;Cardiovascular Disease;Machine Learning;DB-SCAN;Ensemble Model;k-fold cross validation},
Available at: https://doi.org/10.1109/OCIT59427.2023.10430692
This a project where Chronic Kidney Disease and Diabetes is predicted using an ensemble approach, the base classifiers used in the model are Decision Tree (DT), Logistic Regression (LR) and Neural Networks (NN). I have published research articles on this topic and I made it public to help others in creating new machine learning frameworks. To know more about this project, visit the article links provided below.
https://link.springer.com/chapter/10.1007/978-3-031-53728-8_19
Citation
Chaitanya Datta, M., Venkaiah Chowdary, B., Senapati, R. (2024). Multi Disease Prediction Using Ensembling of Distinct Machine Learning and Deep Learning Classifiers. In: Patel, K.K., Santosh, K., Patel, A., Ghosh, A. (eds) Soft Computing and Its Engineering Applications. icSoftComp 2023. Communications in Computer and Information Science, vol 2031. Springer, Cham. https://doi.org/10.1007/978-3-031-53728-8_19
Available at: https://doi.org/10.1007/978-3-031-53728-8_19