Alzheimer's Disease Prediction using Machine Learning Model

Machine Learning combining predictions from multiple algorithms (e.g., Extra Trees, Gradient Boosting, AdaBoost, Random Forest, and LightGBM) with a voting classifier, is the process known as ensemble learning techniques. The ensemble assesses performance measures on the training dataset, such as ac...

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Bibliographic Details
Published in2024 1st International Conference on Cognitive, Green and Ubiquitous Computing (IC-CGU) pp. 1 - 6
Main Authors Deepika, Pattanaik, Srinibas, Puneet
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2024
Subjects
AdaBoost Classifier
Boosting
Forestry
Gradient Boosting Classifier
Machine Learning
Machine learning algorithms
Measurement
Prediction algorithms
Predictive models
Python
Random Forest
Training
Trees Classifier
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DOI10.1109/IC-CGU58078.2024.10530739

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Summary:Machine Learning combining predictions from multiple algorithms (e.g., Extra Trees, Gradient Boosting, AdaBoost, Random Forest, and LightGBM) with a voting classifier, is the process known as ensemble learning techniques. The ensemble assesses performance measures on the training dataset, such as accuracy scores, ROC curves, and classification reports, using cross-validation and both soft and hard voting techniques. Using a range of classifiers aims to capture different features of the data and increase the overall predicted accuracy. The versatility of the code is demonstrated with an XGBoost classifier that has been commented out, which leaves room for further customisation and experimentation. Ensemble learning technique also functions as a potent tool for improving model correctness and reliability, with comprehensive evaluation metrics facilitating informed model selection and decision-making. This ensemble strategy aims to increase the model's overall prediction power by using the unique capabilities of each classifier.
DOI:10.1109/IC-CGU58078.2024.10530739