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 approach. Informatics in Medicine Unlocked, 6:28–35, jan 2017. ISSN 2352-9148. doi: 10.1016/J.IMU.2016.12.004. V. Alves, R. Braga, E. Muratov, C. Andrade, V. M. Alves, R. C. […]
Category: 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 supporting an end-to-end pipeline to predict the stages of Alzheimer’s disease. This is a continuation from here. Further, the practical application, use of libraries, software […]
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 features that negatively impact the performance of the model. It helps to reduce overfitting and training time while improving performance. It is also an important […]
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 the model. Grid-search is a technique used to find the optimal hyperparameters for a model. Ensemble learning involves training multiple models and combining the diverse […]
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 are CDRSB, Alzheimer’s Disease Assessment Scale (ADAS11), Rey Auditory Verbal Learning Test (RAVLT) immediate, MRI of whole brain and age of the subject at the […]
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 different studies (Li et al., 2017), (Goyal et al., 2018), (Azvan et al., 2018). The biomarkers are cognitive tests such as Clinical Dementia Rating Sum […]
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 learning algorithms to find the best model which correctly classifies stages of Alzheimer’s disease. In this implementation, features are selected based on two papers ((Goyal […]
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, type of data, correlation, number of classes and type of problem. The studies suggest building multiple models and select the model with the best performance […]