1. Classification and diagnostic prediction of Alzheimer’s Disease and different type of dementia
Debesh Jha and Kwon Goo-Rak
Department of Information and Communication Engineering, Chosun University, Gwangju, South Korea
and (corresponding author)
Dementia is an increasing health problem and Alzheimer disease is a leading source of dementia accounting for 50-60% of the total cases.
Machine learning methods have been proposed for the classification of Magnetic resonance imaging (MRI) into two groups (i.e. AD and dementia).
The entire MRI is used for the investigation of the pathological brains.
Introduction
Conclusion
We presented a new approach for distinguishing AD and Dementia based on structural MRI.
The result obtained with the Boosted Tree classifier were superior to other classifier.
It can be used as a supplementary tool for the physician for diagnosis of AD vs. dementia.
The performance result with the other single classifier showed that proposed method is robust for automated diagnosis of MR brain image.
The limitation of the work is that the dataset used was small.
Future research should focus on advance feature extraction technique and efficient machine learning algorithm like deep learning method could be implemented for the accurate classification.
Boosted Tree Classifier
Ensemble classifier is used to improve the classification accuracy.
A very efficient algorithm called boosted tree is used as a classifier.
Ensemble classifier builds a many weak classifiers, known as base learner and combines the results of these classifier to yield an outcome.
AdaboostM1 algorithm is used for binary classification.
We try setting minimal parent node size to one quarter of the training data if the boosted stumps give poor performance.
The learning rate of the boosted algorithm is set to 0.1 for the better solution.
References
Classification Methodology
Our issue is a binary class classification .
Entire SMRI is used is used as input image .
The image were downloaded from Harvard medical school webpage.
We have considered different kind of Alzheimer's disease into one group and different dementia into another group.
2D discrete wavelet transform is used for feature extraction.
3 level decomposition is used and approximation coefficient is used as an input image for the classifier.
Haar wavelet is used as filter.
A Boosted tree classifier is used for classification.
6 fold cross validation should the classification accuracy to be 59.6%.
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AdaBoost.M1
Simpler version of boosting algorithm.
Invokes learning algorithm, Weak Learn
Generative Models
Assign equal weight to each training instance
Iteration
i. Applying learning algorithm and storing model
ii. Calculate error, e
iii. If e=0 or e>0.5 terminating for every
instance
If classified correctly, multiplying weight by e/(1-e)
iv. Normalize weight
Until stop
Digital Media Computing Lab, Chosun University