Abstract
Breast cancer is one of the major type of cancer which is the leading cause of death in women. The research work is carried out on the real data of patient records obtained from HealthCare Global Enterprises Ltd (HCG) hospitals. The work analyzes the four major class variables in the dataset, namely death, progression, recurrence and metastasis. The influence of the same 11 predictor variables is explored for each of the class. Various machine algorithms namely Support Vector Machine, Decision Tree, Multi-layer Perceptron and Naive Bayes have been explored for classification of the patient data into various classes. The imbalance in the data is handled using an over sampling technique. The contribution of various attributes in classifying the instances into different classes is also being explored. The model helps in predicting various factors and thus helps in early diagnosis in the breast cancer.
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Shastri, S.S., Nair, P.C., Gupta, D., Nayar, R.C., Rao, R., Ram, A. (2018). Breast Cancer Diagnosis and Prognosis Using Machine Learning Techniques. In: Thampi, S., Mitra, S., Mukhopadhyay, J., Li, KC., James, A., Berretti, S. (eds) Intelligent Systems Technologies and Applications. ISTA 2017. Advances in Intelligent Systems and Computing, vol 683. Springer, Cham. https://doi.org/10.1007/978-3-319-68385-0_28
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DOI: https://doi.org/10.1007/978-3-319-68385-0_28
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