Research Publications
Cardiovascular Disease Prediction Using Supervised Ensemble Machine Learning and Shapley Values
Journal of Emerging Investigators - JEI
https://doi.org/10.59720/23-257
This research is focused on evaluating multiple machine learning and deep learning algorithms to predict the onset of CVD. Shapley values were utilized to identify the risk factors that contributed most to the classification decision with XGBoost, demonstrating the high impact of systolic blood pressure and age on CVD, which aligned with findings in the field of clinical research.
Retinal Disease Prediction Using Deep Convolutional Neural Networks
International Journal of High School Research - IJHSR
Six state-of-the-art models from the residual networks (ResNet), efficient neural networks (ENet), and Inception deep learning architectures, with varying operation sizes, parameters, and features, were developed by training using optimal parameters to predict multiple retinal diseases simultaneously.
COVID-19 Forecasting Using Recurrent NeuralNetwork and Machine Learning
Journal of Student Research - JSR
https://doi.org/10.47611/jsrhs.v13i2.6799
This research presents the modeling and prediction of the combined COVID-19 variant infection trends using the Holt-Winters exponential smoothing and seasonal auto-regressive inte-grated moving average with exogenous factors (SARIMAX) time-series machine learning models and recurrent neural network (RNN) long short-term memory (LSTM) model.