Prediction of Breast Cancer Using Machine Learning Technique

The prognosis and survival chances for people with breast cancer can be significantly improved by an early diagnosis. Therefore, it is crucial to accurately identify malignant tumors nowadays, it has become a frequent health problem and its occurrence is also increased and has high morality. It is also increased due to unawareness and change in the lifestyle of women. It is quite difficult to detect it in the early stage. It is also the deadliest disease after lung cancer. The most optimal machine learning technique to utilize to diagnose a certain disease is still a debate because different things can affect how accurate the results are. Hence, it is mandatory to devote effort in building up a strategy that produces fewer mistakes while improving precision. The research compares four algorithms SVM, Logistic Regression, Random Forest, and KNN—that prognosis the course of breast cancer using various datasets. Following a precise comparison of our models, we discovered that KNN outperformed all other algorithms and had a better efficiency of 97.8%. And, KNN has proven to be effective in predicting and diagnosing breast cancer and gives the best results in terms of accuracy and precision. Improved accuracy by using a variety of algorithms on the basis of the data set and model’s predictions also did a fantastic statistical analysis.