Department Of Electronics And Communication Engineering, Bannari Amman Institute Of Technology, Sathyamangalam – 638401, India.
Abstract
Eventhough People İn This World Are Well-Educated And Sophisticated, Cancer İs Still A Deadly Disease Throughout The World. Amidst This, Breast Cancer İs A Major Cause Of Mortality Among Women. This Shows That There İs Always A Need For The Earlier Tumor Detection Of Breast Cancer. The Paper Utilizes The K-Strongest Strength (Kss) Algorithmfor Breast Cancer Detection. The Employed Kss Algorithm İs İnfluencedby The Law Of Universal Gravitation Analogy And İs Characterized Similarly To The Standard K-Nearest Neighbor (Knn) Algorithm. The Algorithm İs Evaluated Using The Dataset Of Breast Cancer Wisconsin Classification (WDBC) Data. This İnput Data İs Preprocessed And Checked For Any Outliers Followed By Their Removal. Thereafter, The Preprocessed Data İs Applied With The Kss Algorithm For Getting A Better Result Of 97.08% Accuracy. The Obtained Results Are Then Compared With The Standard Benchmark Algorithms Such As Knn And Multi-Layer Perceptron Algorithms For Checking The Robustness Of The Kss-LOF Classifier.
S R, S. C., & Rajaguru, H. (2021). Effectivebreast Tumor Classificationusing KStrongest Strength With Local Outlier Factor Algorithm. Int. J. of Aquatic Science, 12(3), 1596-1603.
MLA
Sannasi Chakravarthy S R; Harikumar Rajaguru. "Effectivebreast Tumor Classificationusing KStrongest Strength With Local Outlier Factor Algorithm". Int. J. of Aquatic Science, 12, 3, 2021, 1596-1603.
HARVARD
S R, S. C., Rajaguru, H. (2021). 'Effectivebreast Tumor Classificationusing KStrongest Strength With Local Outlier Factor Algorithm', Int. J. of Aquatic Science, 12(3), pp. 1596-1603.
VANCOUVER
S R, S. C., Rajaguru, H. Effectivebreast Tumor Classificationusing KStrongest Strength With Local Outlier Factor Algorithm. Int. J. of Aquatic Science, 2021; 12(3): 1596-1603.
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