Alzheimer’s disease can be diagnosed from healthcare information using machine learning

Kailasam, Saikumar (2022) Alzheimer’s disease can be diagnosed from healthcare information using machine learning. Masters thesis, Dublin, National College of Ireland.

Preview	PDF (Master of Science) Download (1MB) | Preview
Preview	PDF (Configuration manual) Download (1MB) | Preview

Abstract

Alzheimer’s disease appears to be one of a number of neurological conditions. Initially, this symptom appears to be normal, but over time it becomes more problematic. Alzheimer’s disease is a well-known form of dementia. Alzheimer’s is a tough disease to treat because there is no cure, and there is no known treatment for the disease. However, it is only later in the disease progression that the ailment is properly diagnosed. There are many ways in which early detection of an illness might delay its progression or symptoms. Predicting Alzheimer’s disease based on parameters such as MMSE scores and the frequency of visits to the doctor is done using fully automated algorithms, such as those used in this study. For the prediction of continuous cognitive variables (AD,CN,LMCI) and categorical classification of three AD phases, machine learning methods including Ada boost, light gradient boosting algorithm, Artificial neural networks (ANN),Ada Boost, Bidirectional LSTM networks and Vote classifiers are utilized. Using data from the ADNI Clinical study, these forecasts have been developed. Metrics that can be used for evaluation include R2, RMSE; Accuracy; Precision; Recall; and F1-score. An approach that uses Vote classifier achieves an accuracy of 93 percent and the more recall values, making it more resilient than other techniques.Using multi-modal ensembles and other cutting-edge methodologies like Bayesian inference can increase model accuracy.

Item Type:	Thesis (Masters)
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > Biomedical engineering Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning H Social Sciences > HM Sociology > Information Science > Communication > Medical Informatics
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Tamara Malone
Date Deposited:	20 Feb 2023 14:48
Last Modified:	02 Mar 2023 11:50
URI:	https://norma.ncirl.ie/id/eprint/6196

Actions (login required)

View Item