Prediction of Pneumonia Disease of Newborn Baby Based on Statistical Analysis of Maternal Condition Using Machine Learning Approach

Abstract

Pneumonia is one of the common diseases amongst children in Bangladesh. Many children die from pneumonia in Bangladesh. Pneumonia is an infection that infects the air sacs in one or both lungs. In Bangladesh, nearly 50,000 children die of pneumonia every year. For diseases forecasting, Machine learning algorithms are popular and used extensively. Machine Learning allows us to fulfill such a task with much consequence. We established our dataset from the particular obtainable from our survey. For prognosticating pneumonia, we employed six traditional Machine Learning algorithms. They are K- Nearest Neighbor (KNN), Naive Bayes classifier, Decision Tree, Support Vector Machine (SVM), Neural Network algorithm, and Random Forest. For implementing these algorithms, we applied Scikit-leam, Pandas, NumPy, and for visualizing our data, we have used Matplotlib and seaborn. By proper interpretation, we considered the best performing algorithm for the prediction of pneumonia. We have measured to classify whether pneumonia declines under pneumonia (Positive) and pneumonia (Negative) class. Among all the algorithms, we have chosen the best algorithm which is provided us best accuracy and F1-score. By the best accomplishing algorithm, our model can predict pneumonia quite well.