Alzheimer's disease is an irreparable, degenerative disease with ongoing loss of functions of the brain. Currently, there is no medicine or treatment present to stop or slow down the progression.
The identification of different stages for diagnosis require a combination of clinical data, complex cognitive tests, radiology imaging, demographic information, time and highly skilled physicians. Recent machine learning techniques can help to provide a process to extract insights and improve the quality of life for the patients and assist the physicians.
In this project, various machine learning techniques such as feature selection, feature engineering, dealing with imbalanced data, imputation of missing values and standardization are applied. Multiple algorithms are also compared before performing random grid search to tune hyperparameters for classifiers and developing an ensemble learner to classify three clinical stages (normal, mild cognitive impairment and dementia).
Cognitive tests, magnetic resonance imaging of left hippocampus and cortical thickness of right entorhinal are discovered to be important features for prediction.
This finding is similar to that reported in a number of studies. The model can equally distinguish between a class and other classes with an average area under the receiver operating characteristics score of 0.83.
This is within the range of the evaluation metric of existing state-of-the-art models.
A web-based application is developed and deployed to the cloud to address the gap for the user to benefit from the developed model. This result in an end-to-end pipeline that will empower the user with a practical application and contribute to the active research in the area.
You can read more about it here