Breast Cancer Recurrence Prediction Using Data Mining
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Abstract
Today, the Healthcare sector is generating a vast amount of meticulously detailed information on individuals and their medical issues. Medical data may be investigated for hidden patterns or connections using data mining techniques. The spread and return of the illness are the main causes of breast cancer fatalities. One of the most important areas of study in the field of medicine is the early diagnosis of breast cancer. Healthcare businesses can benefit from data mining by forecasting patient illnesses and behaviour trends. This is performed by analyzing data from many angles and by using data mining to find hidden patterns and associations between seemingly unrelated bits of information. Months after the original diagnosis and breast cancer therapy, a metastatic recurrence of the disease may happen. Only a few number of techniques have been researched in the state of the art due to the challenges associated with early breast cancer recurrence prediction during its medication. The aim of the research is to review the work done by different researchers in this field and to compare the various traditional data mining classifiers (Logistic Regression, Naïve Bayes, K-Nearest Neighbours, Decision Tree, Support Vector Machines, and Random Forest) applied to Breast Cancer Data Set from UCI Machine Learning Repository. Out of these, the best results were given by Random Forest Algorithm with 98.4% accuracy.