Probabilistic Artificial Neural Networks for Malignant Melanoma Prognosis

摘要

Artificial Neural networks (ANNs) have found applications in a wide variety of medical problems and have proved successful for non-linear regression and classification. This paper details a novel and flexible probabilistic non-linear ANN model for the prediction of conditional survival probability of malignant melanoma patients. Hazard and probability density functions are also estimated. The model is trained using the log-likelihood function, and generalisation has been addressed. Unrestricted by assumptions that are unrealistic or parametric forms that are difficult to justify, the model thereby attains advantage over traditional statistical models. Furthermore, an estimate of the variance-covariance matrix is obtained using the asymptotic Fisher information matrix. Implemented in an Excel® spreadsheet, the model's user-friendly design further adds to its flexibility, with much potential for use by statisticians as well as researchers.