  Cancer analysis and prediction is the utmost important research field for
well-being of humankind. The Cancer data are analyzed and predicted using
machine learning algorithms. Most of the researcher claims the accuracy of the
predicted results within 99%. However, we show that machine learning algorithms
can easily predict with an accuracy of 100% on Wisconsin Diagnostic Breast
Cancer dataset. We show that the method of gaining accuracy is an unethical
approach that we can easily mislead the algorithms. In this paper, we exploit
the weakness of Machine Learning algorithms. We perform extensive experiments
for the correctness of our results to exploit the weakness of machine learning
algorithms. The methods are rigorously evaluated to validate our claim. In
addition, this paper focuses on correctness of accuracy. This paper report
three key outcomes of the experiments, namely, correctness of accuracies,
significance of minimum accuracy, and correctness of machine learning
algorithms.
