ISSN 2394-5125
 

Research Article 


A HYBRID SEGMENTATION BASED MAJORITY VOTING AND CLASSIFICATION FRAMEWORK FOR ALZHEIMER DISEASE PREDICTION

I. Murali Krishna, Dr. Challa Narsimham, Dr. A. S. N. Chakravarthy..

Abstract
Machine learning models play a vital role in the Alzheimer's disease (AD) prediction in neurodegenerative brain disease. Several high-dimensional, reliable classification methods have been implemented in recent years to automatically classify the Alzheimer’s disease patterns. Since most conventional machine learning models such as random forest, random tree, neural network, multi-class SVM, etc. are hard to find the feature extraction process and difficult to detect essential features for classifying disease. However, as the number of features space increases, it is difficult to find the essential disease patterns on the training image datasets. Also, most of the conventional image prediction models have high false positive rate for disease classification. To minimize these issues, a hybrid segmentation-based disease prediction model is implemented on the Alzheimer disease database. In this work, a gaussian image features are used to filter the homogeneous and heterogeneous features for image classification process. Simulation results show that the present feature extraction-based segmentation and classification algorithm has high true positive, F-measure, recall and precision than the conventional models on Alzheimer database.

Key words: ADNI feature selection, Alzheimer, classification model.


 
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How to Cite this Article
Pubmed Style

I. Murali Krishna, Dr. Challa Narsimham, Dr. A. S. N. Chakravarthy.. A HYBRID SEGMENTATION BASED MAJORITY VOTING AND CLASSIFICATION FRAMEWORK FOR ALZHEIMER DISEASE PREDICTION. JCR. 2020; 6(6): 376-385. doi:10.31838/jcr.06.6.56


Web Style

I. Murali Krishna, Dr. Challa Narsimham, Dr. A. S. N. Chakravarthy.. A HYBRID SEGMENTATION BASED MAJORITY VOTING AND CLASSIFICATION FRAMEWORK FOR ALZHEIMER DISEASE PREDICTION. http://www.jcreview.com/?mno=16342 [Access: May 31, 2021]. doi:10.31838/jcr.06.6.56


AMA (American Medical Association) Style

I. Murali Krishna, Dr. Challa Narsimham, Dr. A. S. N. Chakravarthy.. A HYBRID SEGMENTATION BASED MAJORITY VOTING AND CLASSIFICATION FRAMEWORK FOR ALZHEIMER DISEASE PREDICTION. JCR. 2020; 6(6): 376-385. doi:10.31838/jcr.06.6.56



Vancouver/ICMJE Style

I. Murali Krishna, Dr. Challa Narsimham, Dr. A. S. N. Chakravarthy.. A HYBRID SEGMENTATION BASED MAJORITY VOTING AND CLASSIFICATION FRAMEWORK FOR ALZHEIMER DISEASE PREDICTION. JCR. (2020), [cited May 31, 2021]; 6(6): 376-385. doi:10.31838/jcr.06.6.56



Harvard Style

I. Murali Krishna, Dr. Challa Narsimham, Dr. A. S. N. Chakravarthy. (2020) A HYBRID SEGMENTATION BASED MAJORITY VOTING AND CLASSIFICATION FRAMEWORK FOR ALZHEIMER DISEASE PREDICTION. JCR, 6 (6), 376-385. doi:10.31838/jcr.06.6.56



Turabian Style

I. Murali Krishna, Dr. Challa Narsimham, Dr. A. S. N. Chakravarthy.. 2020. A HYBRID SEGMENTATION BASED MAJORITY VOTING AND CLASSIFICATION FRAMEWORK FOR ALZHEIMER DISEASE PREDICTION. Journal of Critical Reviews, 6 (6), 376-385. doi:10.31838/jcr.06.6.56



Chicago Style

I. Murali Krishna, Dr. Challa Narsimham, Dr. A. S. N. Chakravarthy.. "A HYBRID SEGMENTATION BASED MAJORITY VOTING AND CLASSIFICATION FRAMEWORK FOR ALZHEIMER DISEASE PREDICTION." Journal of Critical Reviews 6 (2020), 376-385. doi:10.31838/jcr.06.6.56



MLA (The Modern Language Association) Style

I. Murali Krishna, Dr. Challa Narsimham, Dr. A. S. N. Chakravarthy.. "A HYBRID SEGMENTATION BASED MAJORITY VOTING AND CLASSIFICATION FRAMEWORK FOR ALZHEIMER DISEASE PREDICTION." Journal of Critical Reviews 6.6 (2020), 376-385. Print. doi:10.31838/jcr.06.6.56



APA (American Psychological Association) Style

I. Murali Krishna, Dr. Challa Narsimham, Dr. A. S. N. Chakravarthy. (2020) A HYBRID SEGMENTATION BASED MAJORITY VOTING AND CLASSIFICATION FRAMEWORK FOR ALZHEIMER DISEASE PREDICTION. Journal of Critical Reviews, 6 (6), 376-385. doi:10.31838/jcr.06.6.56