Application of modified artificial bee colony algorithm in health condition diagnosis
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In the medical field, it is very important for doctors to make effective and correct decision-making. In order to improve the accuracy of doctors' diagnosis and avoid the misdiagnosis of doctors' intuition, subconscious and incomplete knowledge. ABC-NB algorithm is used in the field of chronic disease diagnosis to improve the diagnostic efficiency and reduce the chance of misjudgment. The artificial bee colony algorithm based on improved scale factor is applied to the selection of chronic disease characteristics, and the data are dimensioned, the redundant and irrelevant features are removed, the convergence speed is improved, and the algorithm is applied to search the global optimal solution. Then, the eigenvalues of the pre-processed data are trained and learned to generate the Bayesian classifier to construct the prediction model. The prediction module displays the diagnostic results for medical staff to assist in the diagnosis and decision making. Experiments show that the model has good flexibility and robustness, can have a stable calculation of the probability of diagnosis of chronic diseases,and it is effective for the diagnosis of medical staff.
Health condition, Feature selection, Artificial bee colony algorithm, Bayesian.