Indian Journal of Innovations and Developments

Open Access Open Access  Restricted Access Subscription Access

A Survey on Prediction of Brain Hemorrhage Using Various Techniques


Affiliations
1 Dept of Computer Science, K.G.College of Arts and Science, Saravanampatti, Coimbatore-641035, Tamil Nadu, India
2 Dept of Information Technology, K.G.College of Arts and Science, Saravanampatti, Coimbatore-641035, Tamil Nadu, India
 

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.
User
Notifications

  • S. Ushanandhini, S. Uma, G. Anisha. Diabetic retinopathy detection and classification techniques. Indian Journal of Innovations and Developments. 2016; 5 (1), 1-4.

  • E. Alexopoulos, G. D. Dounias, K. Vemmos. Medical diagnosis of stroke using inductive machine learning. Machine Learning and Applications: Machine Learning in Medical Applications, 1999; 20-23.

  • U. Balasooriya, M. S. Perera. Intelligent brain hemorrhage diagnosis system. In IT in Medicine and Education (ITME), 2011 International Symposium. IEEE. 2011; 2, pp. 366-370.

  • B. Sharma, K. Venugopalan. Automatic segmentation of brain CT scan image to identify hemorrhages. International Journal of Computer Applications - IJCA, 2012; 40(10), 1-4.

  • J. Y. Choi, S. K. Kim, W. H. Lee, T. K. Yoo, D. W. Kim. A survival prediction model of rats in hemorrhagic shock using the random forest classifier. In2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE. 2012; 5570-5573.

  • U. Balasooriya, M. S. Perera. Intelligent brain hemorrhage diagnosis using artificial neural networks. In Business Engineering and Industrial Applications Colloquium (BEIAC), IEEE 2012; 128-133.

  • 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 Sep; 467-471.

  • M. M. Kyaw. Pre-segmentation for the computer aided diagnosis system. International Journal of Computer Science & Information Technology, 2013; 5(1), 79.

  • H. S. Bhadauria, M. L. Dewal. Intracranial hemorrhage detection using spatial fuzzy c-mean and region-based active contour on brain CT imaging. Signal, Image and Video Processing. 2014; 8(2), 357-364.

  • E. S. A. El-Dahshan, H. M. Mohsen, K. Revett, A. B. M. Salem. Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm. Expert systems with Applications. 2014; 41(11), 5526-5545.

  • B. Shahangian, H. Pourghassem. Automatic brain hemorrhage segmentation and classification algorithm based on weighted grayscale histogram feature in a hierarchical classification structure. Biocybernetics and Biomedical Engineering, 2016; 36(1), 217-232.


Abstract Views: 310

PDF Views: 0




  • A Survey on Prediction of Brain Hemorrhage Using Various Techniques

Abstract Views: 310  |  PDF Views: 0

Authors

C. Dheeba
Dept of Computer Science, K.G.College of Arts and Science, Saravanampatti, Coimbatore-641035, Tamil Nadu, India
S. Vidhya
Dept of Information Technology, K.G.College of Arts and Science, Saravanampatti, Coimbatore-641035, Tamil Nadu, India

Abstract


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.

References