Efficient disease identification using symptom-based ensemble models and bayes-search optimization

Abstract

Symptom-based disease identification is crucial to the diagnosis of the disease at the early stage. Usage of traditional stacking and blending models i.e., with default values of the models cannot handle the multi-classification data properly. Some of the existing researchers also implemented tuning with the help of a grid search approach but it consumes more time because it checks all the possible combinations. Suppose if the model has n estimators with k values it needs to check (n*k)! elements combination, this makes the learning time high. The proposed model chooses the estimators to train the model with in a considerable amount of time using an advanced tuning technique known as “Bayes-Search” on an ensemble random forest and traditional, support vector machine. The advantage of this model is its capability to store the best evaluation metrics from the previous model and utilise them to store the new model. This model chooses the values of the estimator based on the probability of selection, which reduces the elements in search space i.e., (< (n-k)!). The proposed model defines the objective function with a minimum error rate and predicts the error rate with the selected estimators for different distributions. The model depending on the predicted value decides whether to store the value or to return the value to the optimizer. The Bayes search optimization has achieved +9.21% accuracy than the grid search approach. Among the two approaches random forest has achieved good accuracy and less loss using Bayes search with cross-validation.