Analysis and Classification of Breast Cancer Disease Via Different Datasets and Classifier Models

Abstract

Nowadays, Tumour is one of the important reasons of human death worldwide, producing about 9.6 million people in 2018. BC (breast cancer) is the common reason for cancer deaths in females. BC is a type of cancer that can be treated when detected early. The main motive of this analysis is to detect cancer early in life using ML (machine learning) techniques. The features of the people included in the WDBC (Wisconsin diagnostic breast cancer) and Coimbra BC datasets were classified by SVOF-KNN, KNN, and Naïve Bayes techniques. The pre-processing data phase was applied to the datasets before classification. After the data pre-processing steps, three classification methods were applied to the data. Specificity and Sensitivity rates were used to calculate the success of the techniques. As an outcome of the BC diagnosis classification, the SVOF-KNN technique was found with a 91 percent specificity rate and 90 percent sensitivity rate. When the outcomes attained from feature extraction and selection are calculated. It is seen that feature extraction, selection, and data pre-processing techniques improve the specificity and sensitivity rate of the detection system.

Algorithmic Fairness and Bias in Machine Learning Systems

“…[3] The author discusses the difficulties in attaining justice in algorithmic decision-making systems and looks into the biases found in recidivism prediction algorithms. [4] [16] The idea of fairness via awareness is introduced in this landmark study, which also suggests a paradigm for attaining algorithmic fairness in decision-making procedures. [5] The inherent difficulties and trade-offs involved with defining and establishing fairness in algorithmic decision-making systems are discussed by the writers.…”