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.

I. INTRODUCTION

Pneumonia is a lung infection that occurs suddenly. It's a fatal illness in which the air bags fill up with infection and other fluids. Pneumonia may be bacterial or viral. Bacterial pneumonia is known to cause more severe symptoms in most cases. The cure for bacterial and viral pneumonia is the most important distinction. Antibiotic therapy treats bacterial pneumonia, whereas viral pneumonia usually improves. It's a disorder that affects people all over the world. A high level of contamination is the main source. In the United States, pneumonia is ranked eighth between the top ten reasons of death. Every year, many children die from pneumonia, reporting for half of all pneumonia fatalities in the country[1].