Indian Journal of Science and Technology
DOI: 10.17485/ijst/2018/v11i3/119179
Year: 2018, Volume: 11, Issue: 3, Pages: 1-6
Original Article
Aritra Basu1*, Rohit Roy1 and N. Savitha2
1 School of Electronics Engineering (SENSE), VIT University, Vellore − 632014, Tamil Nadu, India; [email protected], [email protected]
2 School of Social Science and Languages (SSL), VIT University, Vellore − 632014, Tamil Nadu, India; [email protected]
*Author for correspondence
Aritra Basu,
School of Electronics Engineering (SENSE), VIT University, Vellore − 632014, Tamil Nadu, India; [email protected]
Objective: To suggest an automated diagnostic system for the early detection of breast cancer. Methods: This problem has been addressed by making use of machine learning algorithms that can accurately classify a tumor as either malignant or benign by identifying the minimum number of image features. A comparative study on various classification approaches such as Decision Tree, Support Vector Machine, K-Nearest Neighbor and Random Forest have also been conducted with a focus on cross validation to identify the best performing model. Findings: The study shows that Random Forest classifier gives the maximum accuracy. It also highlights that cross validation and fine tuning are necessary to prevent over fitting of data. Improvements: It has been observed that the selection of parameters play a very important role in correct classification as multicollinearity among attributes can render classifier models ineffective.
Keywords: Breast Cancer, Classification, Cross Validation, Decision Tree, K-Nearest Neighbor, Logistic Regression, Random Forest, Support Vector Machine
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