Predictive Strength of Ensemble Machine Learning Algorithms for the Diagnosis of Large Scale Medical Datasets

Abstract

Machine learning is not a simple technology but an amazing field having more and more to explore. It has a number of real-time applications such as weather forecast, price prediction, gaming, medicine, fraud detection, etc. Machine learning has an increased usage in today's technological world as data is growing in volumes and machine learning is capable of producing mathematical and statistical models that can analyze complex data and generate accurate results. To analyze the scalable performance of the learning algorithms, this chapter utilizes various medical datasets from the UCI Machine Learning repository ranges from smaller to large datasets. The performance of learning algorithms such as naïve Bayes, decision tree, k-nearest neighbor, and stacking ensemble learning method are compared in different evaluation models using metrics such as accuracy, sensitivity, specificity, precision, and f-measure.