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 e-mail: debeshjha1@gmail.com and grkwon@chosun.ac.kr (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%. [1] El-Dahshan, El-Sayed Ahmed, Tamer Hosny, and Abdel-Badeeh M. Salem. "Hybrid intelligent techniques for MRI brain images classification." Digital Signal Processing 20.2 (2010): 433-441 [2] Zhang, Yudong, et al. "A hybrid method for MRI brain image classification." Expert Systems with Applications 38.8 (2011): 10049-10053. [3] Chaplot, Sandeep, L. M. Patnaik, and N. R. Jagannathan. "Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network." Biomedical Signal Processing and Control 1.1 (2006): 86-92. [4] Freund, Yoav, and Robert E. Schapire. "Experiments with a new boosting algorithm." Icml. Vol. 96. 1996. [5] Roe, Byron P., et al. "Boosted decision trees as an alternative to artificial neural networks for particle identification." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 543.2 (2005): 577-584. 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