Unstructured Healthcare Statistics Exploration Consuming Modest Ensemble Machine Learning Methods

Various approaches to classifying emotions can be found inside an ML study. Of course, there are ML algorithms that do exceptionally well on particular text datasets but underwhelm on other, larger datasets. To get over this issue, several classifiers should be combined to boost classification performance across all datasets. In order to select the most effective classifiers for use in an ensemble, this study presents a competitive ensemble classification technique. The effectiveness of sentiment classification should improve as a result of this. Unstructured healthcare data is discussed together with the proposed competitive ensemble classification algorithm. This method employs Twitter data extraction to do multiclass sentiment categorization. They have been categorised into one of five mood groups. In this study, we propose a model that uses a competition between several types of classifiers to determine which ones will work best for ensemble learning. To enhance classification performance, weights are assigned to five separate classifiers, and both the suggested model and a benchmark are assessed. The proposed approach, based on empirical evidence, outperforms baseline categorization methods. To evaluate the effectiveness of the work proposed, experimental results are compared to standard datasets. The outcomes show that the proposed model is superior, with an increase in classification accuracy.