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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]

The purpose of this research is to determine whether the Decision Tree Algorithm or the Gradient Boost Classifier is more accurate in predicting a patient's ailment based on the patient's symptoms by using a healthcare dataset and making a comparison between the two. The research is divided into two categories. That is to say, the Decision Tree method is built in the first group, and in the second group, the Gradient Boost Classifier is analysed and contrasted with it. In this instance, the pretest power analysis was performed at 80%, and the total sample size was 25 in order to achieve a significance level of 0.001. In this study, an effort is made to increase the accuracy of illness prediction based on symptoms by utilising the Decision Tree method, which has an accuracy of 96.10%. This accuracy is higher than the accuracy of the Gradient boost classifier, which is 75%. The p-value for an independent sample test of the Decision tree method is less than 0.05, which indicates a significant result. When the results of the suggested model are compared with those of a Gradient Boost classifier, the proposed model is found to have a greater level of accuracy than the Gradient Boost classifier. [ABSTRACT FROM AUTHOR]