A comparative study to predict breast cancer using machine learning techniques

Shiva Shankar Reddy, Neelima Pilli, Priyadarshini Voosala, Swaroop Ravi Chigurupati


Detection of disease at the starting stage is a very crucial problem. As the population growth increases, the risk of death incurred by breast cancer rises exponentially. Breast cancer is the most common cancer in women, and it is also the most dangerous of all cancers. Deaths because of breast cancer have b een increasing in recent times. Earlier detection of the disease followed by treatment can reduce the risk and increase survival chances. There will be cases where even medical professionals can make mistakes in identifying the disease. This project deals with the detection of Breast cancer using the cell data of the tumor present in the breast. So, with the help of technologies in machine learning and artificial intelligence can substantially improve the diagnosis accuracy. The development of this project is beneficial in medical decision support systems. Several machine learning techniques, namely Adaboost, multi-layer perceptron (MLP) and stacking classifier; were used, and among all the algorithms, the stacking classifier results in the best accuracy. The accuracies 95.6%, 97.1%, and 99.2% respectively.


Adaboost; Artificial intelligence; Breast cancer; Machine learning; Multi-layer perceptron

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DOI: http://doi.org/10.11591/ijeecs.v27.i1.pp171-180


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