Dynamic Features Selection for Heart Disease Classification
The healthcare environment is generally perceived as
being information rich yet knowledge poor. However, there is a lack
of effective analysis tools to discover hidden relationships and trends
in data. In fact, valuable knowledge can be discovered from
application of data mining techniques in healthcare system. In this
study, a proficient methodology for the extraction of significant
patterns from the Coronary Heart Disease warehouses for heart
attack prediction, which unfortunately continues to be a leading cause
of mortality in the whole world, has been presented. For this purpose,
we propose to enumerate dynamically the optimal subsets of the
reduced features of high interest by using rough sets technique
associated to dynamic programming. Therefore, we propose to
validate the classification using Random Forest (RF) decision tree to
identify the risky heart disease cases. This work is based on a large
amount of data collected from several clinical institutions based on
the medical profile of patient. Moreover, the experts- knowledge in
this field has been taken into consideration in order to define the
disease, its risk factors, and to establish significant knowledge
relationships among the medical factors. A computer-aided system is
developed for this purpose based on a population of 525 adults. The
performance of the proposed model is analyzed and evaluated based
on set of benchmark techniques applied in this classification problem.
[1] Brieman L. (2001). Random Forests. In Machine Learning, Kluwer
Academic Publisher, 45(1).
[2] Durairaj, M. & Meena, K. (2011). A Hybrid Prediction System Using
Rough Sets and Artificial Neural Networks, Intl. Journal of Innovative
Technology & Creative Engineering, 1 (7).
[3] Guru, N., Dahiya, A. & Rajpal, N. (2007). Decision Support System for
Heart Disease Diagnosis Using Neural Network. Delhi Business
Review, Vol. 8 (1).
[4] Ho, T.K.. (1998). The random subspace method for constructing
decision forests. IEEE Trans. on Pattern Analysis and Machine
Intelligence, 20(8).
[5] Moudani, W., Shahin, A., Chakik, F. & Mora-Camino, F. (2011).
Dynamic Rough Sets Features Reduction. Intl. Journal of Computer
Science and Information Security, Vol. 9(4).
[6] Rajeswari, K., Vaithiyanathan, V. & Amirtharaj, P. (2011). Prediction of
Risk Score for Heart Disease in India Using Machine Intelligence.
International Conference on Information and Network Technology, Vol.
4.
[7] Patil, S.B. & Kumaraswamy, Y.S. (2009). Intelligent and Effective
Heart Attack Prediction System Using Data Mining and Artificial Neural
Network, European Journal of Scientific Research, Vol. 31 (4), pp.642-
656.
[8] Segrera, S. & Moreno, M. (2005). Multiclassifiers: applications,
methods and architectures. Proc. of Intl. Workshop on Practical
Applications of Agents and Multiagents Systems, 263-271.
[9] Srinivas, K., Kavihta, B. & Govrdhan, A. (2010). Applications of Data
Mining Techniques in Healthcare and Prediction of Heart Attacks.
International Journal on Computer Science and Engineering, Vol. 2 (2).
[1] Brieman L. (2001). Random Forests. In Machine Learning, Kluwer
Academic Publisher, 45(1).
[2] Durairaj, M. & Meena, K. (2011). A Hybrid Prediction System Using
Rough Sets and Artificial Neural Networks, Intl. Journal of Innovative
Technology & Creative Engineering, 1 (7).
[3] Guru, N., Dahiya, A. & Rajpal, N. (2007). Decision Support System for
Heart Disease Diagnosis Using Neural Network. Delhi Business
Review, Vol. 8 (1).
[4] Ho, T.K.. (1998). The random subspace method for constructing
decision forests. IEEE Trans. on Pattern Analysis and Machine
Intelligence, 20(8).
[5] Moudani, W., Shahin, A., Chakik, F. & Mora-Camino, F. (2011).
Dynamic Rough Sets Features Reduction. Intl. Journal of Computer
Science and Information Security, Vol. 9(4).
[6] Rajeswari, K., Vaithiyanathan, V. & Amirtharaj, P. (2011). Prediction of
Risk Score for Heart Disease in India Using Machine Intelligence.
International Conference on Information and Network Technology, Vol.
4.
[7] Patil, S.B. & Kumaraswamy, Y.S. (2009). Intelligent and Effective
Heart Attack Prediction System Using Data Mining and Artificial Neural
Network, European Journal of Scientific Research, Vol. 31 (4), pp.642-
656.
[8] Segrera, S. & Moreno, M. (2005). Multiclassifiers: applications,
methods and architectures. Proc. of Intl. Workshop on Practical
Applications of Agents and Multiagents Systems, 263-271.
[9] Srinivas, K., Kavihta, B. & Govrdhan, A. (2010). Applications of Data
Mining Techniques in Healthcare and Prediction of Heart Attacks.
International Journal on Computer Science and Engineering, Vol. 2 (2).
@article{"International Journal of Medical, Medicine and Health Sciences:56362", author = "Walid MOUDANI", title = "Dynamic Features Selection for Heart Disease Classification", abstract = "The healthcare environment is generally perceived as
being information rich yet knowledge poor. However, there is a lack
of effective analysis tools to discover hidden relationships and trends
in data. In fact, valuable knowledge can be discovered from
application of data mining techniques in healthcare system. In this
study, a proficient methodology for the extraction of significant
patterns from the Coronary Heart Disease warehouses for heart
attack prediction, which unfortunately continues to be a leading cause
of mortality in the whole world, has been presented. For this purpose,
we propose to enumerate dynamically the optimal subsets of the
reduced features of high interest by using rough sets technique
associated to dynamic programming. Therefore, we propose to
validate the classification using Random Forest (RF) decision tree to
identify the risky heart disease cases. This work is based on a large
amount of data collected from several clinical institutions based on
the medical profile of patient. Moreover, the experts- knowledge in
this field has been taken into consideration in order to define the
disease, its risk factors, and to establish significant knowledge
relationships among the medical factors. A computer-aided system is
developed for this purpose based on a population of 525 adults. The
performance of the proposed model is analyzed and evaluated based
on set of benchmark techniques applied in this classification problem.", keywords = "Multi-Classifier Decisions Tree, Features Reduction,
Dynamic Programming, Rough Sets.", volume = "7", number = "2", pages = "106-6", }