Machine Learning Insights for Early Breast Cancer Diagnosis

Breast Cancer (BC) became a major disease among women in the current era. For treatment, survival and to decrease mortality rate detection of disease at early stages plays a significant role. Machine learning (ML), big data analysis, Internet of Things (IoT) and artificial intelligence (AI) are providing better and accurate solutions for early diagnosis of multiple sectors like education, health, military, weather, share market etc. In this work, ML based classifications were used to analysis and predictions. Multiple ML algorithms like J48, Random Tree (RT) and Random Forest (RF) has been used for predictive results. For analysis and predictions, an open dataset has been accessed for this work. And, data pre-processing, normalization has been applied before executing the model. Multiple selection technique has applied in this work to identify and prediction of breast cancer (BC). Two important attributes such as mps (menopausal status) and T_grade (tumor grades) from dataset has been opted to predictions. In this work the accuracy has calculated with results like, FP rate, recall, precision and F-measure. Also, correct and incorrect instances of dataset have been calculated. Perspectives of ML are highlighted to early identification of BC. The results showed that these models can be helpful to early detect the BC and can reduce the mortality rate.