A Review on An Ensemble Learning Approach For Apoplexy Prediction


EOI: 10.11242/viva-tech.01.05.020

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Citation

Mitali Kadam, Poonav Kuchekar, Srushti Deopurkar, Sunita Naik, "A Review on An Ensemble Learning Approach For Apoplexy Prediction", VIVA-IJRI Volume 1, Issue 6, Article 20, pp. 1-5, 2023. Published by Computer Engineering Department, VIVA Institute of Technology, Virar, India.

Abstract

In recent years stroke are one of the leading causes of death by affecting the central nervous system. The term apoplexy refers to brain stroke. There are different types of strokes, among which ischemic and hemorrhagic majorly damages the central nervous system. In this research work, Machine learning techniques are applied in identifying, classifying and predicting the brain stroke from medical information. The standard dataset is available on Kaggle. The dataset contains 11 attributes and 5000 rows. According to research, Stacking proved to be the best with 98.9% of accuracy and 97.4% of recall, precision and F-measure. The stacking was composed of single classifier as base learners and Logistic Regression or Random Forest was used as meta learner. Decision Tree, K-Nearest Neighbor, SVM, Adaboost, Logistic Regression etc was used as a single model or as an ensemble model. The purpose of this paper is to present a survey on predictive models for Brain Strokes using a machine learning ensemble classifier.

Keywords

Stacking, Meta-learner, Ensemble learning, Base learners

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