Abstract
In the field of medicine, several recent studies have shown the value of Artificial Neural Networks, decision trees, logistic regression are playing a major role as the predictor, and classification methods. The research has been expanded to estimate the incidence of breast, lung, liver, ovarian, cervical, bladder and skin cancer. The main aim of this paper is to develop models of logistic regression, Artificial Neural Networks, and Decision trees using the same input and output variables and to compare their success in predicting breast cancer survival in woman. To find the best model for breast cancer survival, the sensitivity and specificity of all these models are measured and evaluated with their respective confidence intervals and the ROC values.
Highlights
In the field of clinical diagnostics, computer models are playing a prominent role in differentiating between a healthy and an ill patient
To maintain consistency in comparing these implemented models, we considered the accuracy of three techniques when classifying breast cancer survival results
The results indicated that Model-4 yielded better performance using logistic regression, artificial neural networks (ANN), and decision tree methods
Summary
In the field of clinical diagnostics, computer models are playing a prominent role in differentiating between a healthy and an ill patient. The technique of logistic regression is very widely used in data analysis It is considered a well-known model of classification that enables probabilistic decisions to be made and shows promising results on several issues. Four models are developed in this paper considering the same output and the set of input variables using decision trees, logistic regression and artificial neural networks. Performance of these models is assessed in breast cancer survival prediction. Logistic regression eliminated variables like size of the tumor, grade of the cancer, marital status of the patient to be statistically insignificant in prediction survival of women with breast cancer. Survival prediction of breast cancer is the output variable in our current research from the given patient’s age, size of tumor, stage of cancer, treatment administered, and the duration
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