Breast Cancer Screening Using a Modified Inertial Projective Algorithms for Split Feasibility Problems

Table 2

Highest accuracy of ML algorithms after feature engineering.

Machine learning model	Original dataset (%)	Highest accuracy (%)	Data cleaning or feature engineering
Logistic regression	80.46	82.50	Removing two least contributed features, RFE, or removing two least contributed features, correlation matrix
Linear discriminant analysis	78.90	84.50	Removing two least contributed features, RFE, or removing two least contributed features, correlation msatrix
K-nearest neighbors	79.03	82.05	Removing two least contributed features, RFE, or removing two least contributed features, correlation matrix
Classification and regression trees	74.48	82.97	Removing two least contributed features, RFE, or removing two least contributed features, correlation matrix
Gaussian Naive Bayes	78.37	83.73	Removing rows with missing values and outliers
Support vector machines	80.34	83.73	Removing two least contributed features, RFE, or removing two least contributed features, correlation matrix
Algorithm 1 ()	53.89	83.23	Removing rows with missing values and outliers
Algorithm 1 ()	53.89	85.03	Removing rows with missing values and outliers