Breast cancer is the leading cause of death among women around the world. It is a primary malignancy
for which genetic markers have revealed the ability for clinical decision making. It is a genetic disease
that generates due to gene mutations, but the cost of a genetic test is relatively high for a number of
patients in developing nations like India. The results of a genetic test can take a few weeks to determine
cancer. This time duration influences the prognosis of genes since certain patients suffer from a high rate
of malignant cell proliferation. Therefore, a computer-assisted genetic test method (CAGT) is proposed
to detect breast cancer. This test method will predict the gene expressions and convert these expressions
in the state of mutation (under-expression (-1), transition (0) overexpression (1)) and afterwards perform
the classification to get the benign and malignant class in reduced time and cost. In the research work,
machine learning techniques are applied to identify the most responsive genes of breast cancer on the
premises of the clinical report of a patient and generated a CAGT. In the research work, the hard voting
ensemble approach is applied to detect breast cancer on the basis of most responsive genes by CAGT
which leads to improving 3.5% accuracy in cancer classification