This paper describes an intelligent classification system for cancer data. The system employs a hybrid radial basis function (HRRF) network in order to classify cancer data into several classes. The HRBF network is trained using the moving k-means clustering algorithm to position the network's centre and the Given least square (GLS) aigorithm to estimate the network's weights. Two cancer data, i.e. cervical cancer and breast cancer, are used as caw studies. For cervical canccr, the system classifies the data into three classes, i.e. normal, low grade squamous intraepithelial lesion (LSIL) andhigh grade squamous intraepithelial lesion (HSIL). The system praduccs 98.00% accuracy. While for breast cancer, the system classifies the data into benign and malignant data The system produces 98.57% accuracy. The result illustrates the promising capabilities of the system for assisting cervical and breast cancer detection.

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