Abstract: In this paper, we use the diagnosis of breast cytology to demonstrate the applicability of this method to medical diagnosis and decision making. Each of 11 cytological characteristics of breast fine-needle aspirates reported to differ between benign and malignant samples was graded 1 to 10 at the time of sample collection. Nine characteristics were found to differ significantly between benign and malignant samples. Mathematically, these values for each sample were represented by a point in a nine-dimensional space of real variables. We use various different algorithms and also demonstrate the comparison between the algorithms for the classification problem. Finally, an overall accuracy of 99.4048 % is achieved. We only classify 1 % of benign case as malignant. The algorithms used are programmed in python for demonstration purposes. This paper also demonstrates deploying the created model on cloud and building an API for calling the model and verify it.
Keywords: Machine Learning, Decision aid system, Breast Cancer prediction, Logistic Regression, Decision Forest, Neural Network
| DOI: 10.17148/IARJSET.2019.61002