Effectivebreast Tumor Classificationusing KStrongest Strength With Local Outlier Factor Algorithm

Document Type : Primary Research paper


Department Of Electronics And Communication Engineering, Bannari Amman Institute Of Technology, Sathyamangalam – 638401, India.


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.


Volume 12, Issue 3 - Serial Number 3
ICMMNT-2021 International Virtual Conference on Materials, Manufacturing and Nanotechnology, 30th June, 2021.
June 2021
Pages 1596-1603