Novel Machine Learning Approach for Forecasting the Possibility of Recurrence in Community-Acquired Pneumonia

Abstract

Antipsychotic drug usage is known to increase the risk of pneumonia, despite the fact that medications are commonly used to treat schizophrenia. By utilize machine learning (ML) to assemble a model for predicting community-acquired pneumonia (CAP) in schizophrenia patient. The beginning of pneumonia was predicted by eleven factors including gender, age, clozapine usage, drug-drug interactions, dose, length treatment, coughing, and changes in neutrophil and leukocyte counts, blood sugar levels, and body weight. To create the prediction models employed in this work, seven ML techniques were utilized in the study. To assess the overall performance of the model, we employed accuracy, sensitivity, specificity. In comparison to other seven ML methods, RF and DT have results the improved forecasting efficiency. Six other key risk variables were also found, including dose, clozapine usage, medication duration, change in neutrophil or leukocyte count, and drug-drug interaction. Our prediction model could be a helpful device for doctors caring for schizophrenic patients, even though these individuals still run the risk of pneumonia while using anti-psychotic medications.