Question

An effective ensemble model for performance optimization and predictive analytics in the healthcare

Answer 1

Explanation:

Various ensemble learning techniques like boosting, adaptive boosting, bagging and stacking have conventionally revealed potential and promising end results in enhancing the predictive accuracy involving classification. Recently the ensemble techniques have been extensively used in various applications and domains on account of the progression and improvement in the distributed computing approaches and also the computational efficiency. There exists a variety of the machine learning algorithms and techniques for the class imbalance problems and moreover focus is required to analyse, evaluate, enhance and optimize the performance metrics that includes sensitivity and the specificity in the classification techniques. In this paper a novel stacked ensemble model is demonstrated to analyze and optimize specificity and sensitivity on diabetes dataset collected from a private hospital. The diabetes dataset is being analysed to determine and predict whether the individual might be readmitted to the hospital considering various feature sets. The proposed stacked ensemble model includes the Support Vector Machine (SVM), Extreme Gradient Descent and the Random Forest in the level 1 and for the next level the neural network classifier is used. By using this technique the sensitivity and specificity can be greatly optimized with regard to each other without negotiating the accuracy of the model.

Answer 2

Answer:

Various ensemble learning techniques like boosting, adaptive boosting, bagging and stacking have conventionally revealed potential and promising end results in enhancing the predictive accuracy involving classification.

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