References for the project C. Aditya and M. S. Pande. Devising an interpretable calibrated scale to quantitatively assess the dementia stage of subjects with alzheimer’s disease: A machine learning…
Discussion The project has fully answered research questions after implementing different machine learning techniques and evaluating the performance of the models. The developed model and user-friendly web application contribute to…
Use Feature Selection Techniques and Build an Ensemble of Classification Models Feature selection is an automatic or manual process to select features which contribute to the prediction and remove irrelevant…
Fine Tuning of an Ensemble of Classification Models Using Random Grid Search Parameters for a model are learned during the training while hyperparameters are set to control the implementation of…
Create a Web-based Application using XGBoost The implementation uses six features which are important to classify the stages of the disease and XGBoost to develop the web-based application. The features…
Build a Classification Model using Tree-Based Algorithms In this implementation, selected features to build classification model include subject’s education, gender, age and a small list of biomarkers as discussed in…
Build a Classification Model using Monthly Changes in Radiology Imaging and Clinical Data Multiple studies such as (Lahmiri and Shmuel, 2018) and (Zhang and Sejdi´c, 2019) typically apply various machine…
Implementation, Evaluation and Results of Alzheimers Disease Progression Models and Development of Web-based Application Introduction There are few conditions and perquisites for building classification models e.g., sample size of data,…