An automated multi-classification of communicable diseases using ensemble learning for disease surveillance

Communicable diseases are considered significant global health concern for the public, and their timely detection is crucial for effective prevention and spread control. However, communicable disease data are highly interdependent and complex to analyze using the traditional tools for their automatic detection. On the contrary, machine learning enabled models have shown tremendous potential in this regard, enabling rapid and accurate identification of communicable diseases. The objective of this research is to effectively identify as well as classify various communicable diseases using machine learning models in an efficient manner. The data of ten communicable diseases are considered, which are further analyzed by pre-processing, feature selection, and visualization. Later machine learning models such as Random Forest, Gradient Boosting, Decision Tree, Adaptive Boosting (AdaBoost), extreme Gradient Boosting (XGBoost), Extra Tree, Light Gradient Boosting Machine (Light GBM), and Categorical Boosting (CatBoost), along with the hybridization of XGBoost and Random Forest, are being applied, which are further evaluated using the parameters such as precision, false detection rate, recall, negative prediction value, F1 score, accuracy, and Matthew’s correlation coefficient. The confusion matrix of all the models for various classes has also been generated to compute the values of performance metrics. During experimentation, it has been found that the random forest and hybridized model classifier obtained the highest accuracy of 99.9%, Random Forest, Extra Tree Classifier, CatBoost, and Hybrid classifier computed the highest Matthew’s correlation coefficient score of 99.9%, the Gradient Boosting classifier obtained the best false detection rate value with 95.13% and negative predicted value with 189.82. Overall, the research showed that the artificial intelligence techniques have the potential to improve Communicable disease detection extensively, and future research in this area can help to develop more robust and effective disease surveillance and control tools.