Intelligent System to Diagnosis of Pneumonia in Children using Support Vector Machine

Pneumonia causes a high rate of disease and death in infants. This condition impacts the lungs' tiny air sacs and necessitates quick Identification and therapy. Manually, it may take more time to diagnose this disease in children. Machine learning techniques are gaining traction in medical Diagnosis because they can classify data accurately. Using accurate feature selection algorithms to minimize the dimension of datasets is critical to the accuracy of classification algorithms. SVM is a computer algorithm applied to many biological applications with great success. The automated classification of microarray gene expression profiles is a widespread use of support vector machines (SVMs) in biomedicine. This algorithm is used to diagnose pneumonia in children more efficiently and quickly. The proposed intelligent system to diagnose pneumonia in children helps patients diagnose and treat this disease in time. This method would enable specialists to achieve quicker and more precise results, allowing them to provide the best possible treatment. The proposed method for detecting pneumonia in children has a 98.40 percent accuracy rate.