Prediction of disease from symptoms due to climate change using random forest classifier over gradient boosting classifier.

The study's overarching goal is to enhance the accuracy with which the healthcare dataset's Random forest classifier can predict disease from symptoms in the face of climate change. There are two groups in this research. A random forest classifier is first created, then compared to the Gradient boosting classifier. With a sample size of 25, we may achieve a significance level of 0.001 when comparing the models' accuracies to those of these algorithms. The purpose of this research was to determine whether or not the more accurate Random forest classifier (97.16 percent) or the less accurate Gradient boosting classifier (97.1 percent) could be used to predict diseases based on symptoms (75 percent). When using an independent sample test, the Random forest classifier consistently achieves a high level of statistical significance (p0.05). In a head-to-head comparison with a Gradient boosting classifier, the suggested model's results were shown to be more accurate. [ABSTRACT FROM AUTHOR]