Machine Learning-Based Approaches for the Prognosis and Prediction of Multiple Diseases

The rapid progress in machine learning techniques has significantly transformed healthcare which enables the simultaneous and accurate detection of multiple diseases. This paper delves into the application of diverse machine learning algorithms for multi-disease detection by using a comprehensive dataset which focuses on three diseases i.e. diabetes, gonorrhoea, and typhoid. The multi-disease dataset has been meticulously pre-processed and graphically visualized to discern patterns and represent diseases against emotional states/urges and critical feelings. Subsequently, a range of machine learning classifiers which includes logistic regression, Adaboost, random forest, support vector machine, CatBoost, Light Gradient Boosting Classifier, Naïve Bayes, XGBoost, KNN, and Decision Tree, are trained on this dataset. Their performance across these different classes is rigorously evaluated using various parameters such as accuracy, F1 score, recall, and precision. During execution, Adaboost emerged as the top performer, by achieving an impressive accuracy of 94.37% and maintaining a precision, recall, and F1 score of 0.94, which indicates its robustness in multi-disease detection.