Introduction: Disease-causing organisms have shown to be adaptive in nature, and for a proper disease diagnosis, the need for disease detection system becomes paramount. Varieties of different computer-aided algorithms channeled at disease diagnosis were proposed to solve the problems associated with the voluminous diseases reported and recorded. Although these algorithms have proven successful, the negative selection algorithm provides a new pathway to adequately distinguish disease-impaired patients from the healthy ones. The Variable Detector (V-Detector) by generating sets of detectors randomly with the aim of maximizing the coverage area of each detectors, is compared with sequential minimal optimization (SMO), multilayer perceptron (MLP), and nonnested generalized exemplars (NNGEs) on the detection of diseases. Experimental results shows that the V-Detector generated the highest detection rates of 98.95% and 74.44% for Breast Cancer Wisconsin and BUPA Liver Disorder data sets and also performed significantly with the biomedical data at detection rate of 71.64%. Thus, the V-Detector can achieve highest detection rates and lowest false alarm rates.