Variable Selection in Genetic Algorithm Model with Logistic Regression for Prediction of Progression to Diseases

摘要

Earlier risk assessment and identification of different diseases is the most crucial issue for avoiding and lowering their progression. The researchers typically used the statistical comparative analysis or step-by-step methods of feature selection using regression techniques to estimate the risk factors of diseases. The outcomes from these methods emphasized on individual risk factors separately. A combination of factors, however, is likely to affect the development of disease rather than just anyone alone. Genetic algorithms (GA) can be useful and efficient for searching a combination of factors for the fast diagnosis with best accuracies, especially for a large number of complex and poorly understood factors, as in the case in the prediction of disease development. Our proposed model shows the potential for the application of GA in diagnosing disease and predicting accuracy. Our proposed model demonstrated that the amalgamation of a small subset of input features produces the optimum performance than the use of all the single significant features individually. This model not only predicts the best feature sets and accuracy but also overcome the problem of missing values present in the dataset. Variables more frequently selected by LR might be more important for the prediction of disease development and accuracies by GA.