An Extensive Survey on Artificial neural Network Based Cancer Prediction Using Soft-Computing Approach

Abstract

Soft Computing is a branch of artificial computational intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to “learn” from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to medical applications, especially those that depend on complex proteomic and genomic measurements. As a result, computational intelligence is frequently used in cancer diagnosis and detection. More recently soft computing has been applied to cancer prognosis and prediction. This latter approach is particularly interesting as it is part of a growing trend towards personalized, predictive medicine. In assembling this review we conducted a broad survey of the different types of soft computing techniques being used, the types of data being integrated and the performance of these methods in cancer prediction and prognosis. A number of trends are noted, including a growing dependence on protein biomarkers and microarray data, a strong bias towards applications in prostate and breast cancer, and a heavy reliance on “older” technologies such artificial neural networks (ANNs) instead of more recently developed or more easily interpretable soft computing techniques. A number of published studies also appear to lack an appropriate level of validation or testing. Among the better designed and validated studies it is clear that soft computing techniques can be used to substantially (15–25%) improve the accuracy of predicting cancer susceptibility, recurrence and mortality. At a more fundamental level, it is also evident that computational intelligence is also helping to improve our basic understanding of cancer development and progression.