An ensemble approach to predict acute appendicitis

There is currently no international treaty on how to treat human appendicitis. Given the scarcity of data-driven studies on this species and the lack of clear international acute appendicitis (AA) management recommendations. Heuristic scoring methods are often used in clinical practice to determine patient urgency. This study's main purpose is to see if using ensemble approaches can enhance prediction responses. The target variable in 106 occurrences of the AA dataset published at the Keel repository was predicted using an ensemble of classifiers. Majority voting and weighted averaging are the ensemble methodologies employed in this study, and they are applied to a variety of basic classification algorithms. The accuracy, misclassification rate, sensitivity, specificity, precision, f1-score, false positive and negative rates, and Mathew's correlation coefficient of these techniques were all evaluated in the Jupyter Python environment. Combining machine learning and deep learning technologies can help with AA diagnosis and care, paving the way for more personalized medical decision-making. To finish a clinical decision support system, more validation studies are required further.