A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Dheeba, C.
- A Survey on Prediction of Brain Hemorrhage Using Various Techniques
Authors
1 Dept of Computer Science, K.G.College of Arts and Science, Saravanampatti, Coimbatore-641035, Tamil Nadu, IN
2 Dept of Information Technology, K.G.College of Arts and Science, Saravanampatti, Coimbatore-641035, Tamil Nadu, IN
Source
Indian Journal of Innovations and Developments, Vol 5, No 6 (2016), Pagination: 1-3Abstract
Objectives: The main objective of this work is to predict Subarachnoid haemorrhage (SAH) using machine learning techniques and analyzing the classification performance of various existing machine learning algorithms.
Methods: Diagnosing theSubarachnoid haemorrhage can be done efficiently by various machine learning techniques. Purpose of using Machine learning technique is to focus on factors that influence the prediction performance.
Findings: Subarachnoid haemorrhage is a stroke which is recognised by the occurrence of blood in subarachnoid space. Diagnosis of such potential disease becomes more important in the medical research area. Most widely used data mining methods for prediction tasks are decision rules, naïve Bayesian classifiers, support vector machines, Bayesian networks, and nearest neighbors. Some of the methods namely boosting, bagging and genetic algorithms have limited usage in the prediction.
Application/Improvements: The finding of this work shows that random forest classifier provides effective classification result than other machine learning techniques.
Keywords
Subarachnoid Haemorrhage, Machine Learning Techniques, Support Vector Machine, Naïve Bayesian Classifiers, Bayesian Networks, Genetic Algorithm.Full Text
References
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- A Novel Machine Learning Approach for the Prediction of Subarachnoid Hemorrhage
Authors
1 Dept of Computer Science, K.G.College of Arts and Science, Saravanampatti, Coimbatore-641035, Tamil Nadu, IN
2 Dept of Information Technology, K.G.College of Arts and Science, Saravanampatti, Coimbatore-641035, Tamil Nadu, IN
Source
Indian Journal of Education and Information Management, Vol 5, No 3 (2016), Pagination: 1-8Abstract
Objectives: To predict outcome of patients with Subarachnoid Hemorrhage effectively by using novel ensemble classification method.
Methods: The different machine learning approaches are used to improve the outcome of patients with SAH prediction. One of such approach utilizes random forest classifier which is used for enhancing the prediction accuracy.
Findings: The outcome of patients with Subarachnoid Hemorrhage (SAH) prediction is helpful for guiding and caring patients. Such type of prediction is the most important in medical research area. Mostly SAH prediction is achieved by classification techniques such as decision rules, naive Bayesian classifiers, support vector machines, nearest neighbor classifiers and etc. However, these classifiers are not efficient for higher number of training cases.
Application/Improvements: In this paper, we propose a novel ensemble classification technique for effective classification. In which, a random forest classifier is introduced for providing efficient classification by integrating various machine learning algorithms. The algorithms used are C4.5, REPTree, and PART. The experimental results show that the best ensemble classifier and effectiveness of the random forest algorithm.
Keywords
Subarachnoid Hemorrhage, Decision Tree Classifier, Support Vector Machine, Naive Bayesian Classifier, Nearest Neighbor Classifier, Random Forest Algorithm.Full Text
References
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- B. Sharma, K. Venugopalan. Automatic segmentation of brain CT scan image to identify hemorrhages. International Journal of Computer Applications.2012; 40(10), 1-4.
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- B.Shahangian, H. Pourghassem. Automatic brain hemorrhage segmentation and classification in CT scan images. In Machine Vision and Image Processing (MVIP), 2013 8th Iranian Conference on IEEE, 2013, September, 467-471.
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