Ensemble modeling coupled with six element concentrations in human blood for cancer diagnosis

Six important metal contents (i.e., zinc, barium, magnesium, calcium, copper, and selenium) in blood samples coupled with an ensemble classification algorithm have been used for the classification of normal people and cancer patients. A dataset containing 42 healthy samples and 32 cancer samples was used for experiment. The prediction results from this method outperformed those from the newly developed support vector machine, i.e., a sensitivity of 100%, a specificity of 95.2%, and an overall accuracy of 98.6%. It seems that ELDA coupled with blood element analysis can serve as a valuable tool for diagnosing cancer in clinical practice.