Nowadays, the most common type of cancer in women worldwide is Breast Cancer (BC). BC may be detected at early stage itself using Mammograms, probably before it's spread. Recurrent BC could occur months or years after initial treatment. Cancer may occur in the same place or spread to different areas due to local or distant recurrence. Early-stage treatment is done not only to cure BC but additionally facilitate in preventing its recurrence/ repetition. In predicting the early stage of BC, a machine learning (ML) technique has been used by most of the researcher. so, the present study we focus on a review of different ML techniques which predicts the recurrence of BC and identified the issues over the past decades. Also summarized the obtained results by the researcher for evaluating their predictive model performance. The study scope, results, merits, and demerits of earlier studies have been discussed. Later, gives deep insights of learning technique and then recommended a possible solution for further improvement for BC recurrence prediction.
|Name||2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2021|
|Conference||9th IEEE International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2021|
|Period||3/09/21 → 4/09/21|