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An Investigation for Detection of Breast Cancer using Data Mining Classification Techniques


Affiliations
1 IKG Punjab Technical University, Jalandhar, Punjab, India
2 Beant College of Engineering and Technology, Gurdaspur, Punjab, India
3 Radiant Institute of Engineering and Technology, Abohar, Punjab, India
 

Breast cancer is one of the curses for women. Breast cancer caused deaths. It is the second most common cause. 1 in 28 women develop breast cancer during her lifetime in India. Urban/Rural ratio in a lifetime of women for the risk of developing breast cancer is 60:22. High risk group in India has the average age of 43-46 years whereas the same in the west is 53-57 years. The main objective of this paper is to investigate the performance of different classification techniques. Here, the breast cancer data available from the Wisconsin dataset from UCI machine learning is analyzed. In this experiment, Comparison of three different classification techniques have been done in Weka software and comparison results shows that Sequential Minimal Optimisation (SMO) has higher prediction accuracy i.e. 95.8512 % than methods Instance based K-Nearest neighbours classifier ( IBK) and Best First (BF) Tree method.

Keywords

Breast Cancer, Data Mining, Data Mining Classification Techniques.
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  • An Investigation for Detection of Breast Cancer using Data Mining Classification Techniques

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Authors

Sonu Bala Garg
IKG Punjab Technical University, Jalandhar, Punjab, India
Ajay Kumar Mahajan
Beant College of Engineering and Technology, Gurdaspur, Punjab, India
T. S. Kamal
Radiant Institute of Engineering and Technology, Abohar, Punjab, India

Abstract


Breast cancer is one of the curses for women. Breast cancer caused deaths. It is the second most common cause. 1 in 28 women develop breast cancer during her lifetime in India. Urban/Rural ratio in a lifetime of women for the risk of developing breast cancer is 60:22. High risk group in India has the average age of 43-46 years whereas the same in the west is 53-57 years. The main objective of this paper is to investigate the performance of different classification techniques. Here, the breast cancer data available from the Wisconsin dataset from UCI machine learning is analyzed. In this experiment, Comparison of three different classification techniques have been done in Weka software and comparison results shows that Sequential Minimal Optimisation (SMO) has higher prediction accuracy i.e. 95.8512 % than methods Instance based K-Nearest neighbours classifier ( IBK) and Best First (BF) Tree method.

Keywords


Breast Cancer, Data Mining, Data Mining Classification Techniques.

References