Exploring machine learning algorithms to predict health risks and outcomes
Senior Data Engineer
Research Article
World Journal of Advanced Research and Reviews, 2020, 07(03), 313–327
Publication history:
Received on 07 September 2020; revised on 20 September 2020; accepted on 24 September 2020
Abstract:
This study investigates the pertinence of machine learning techniques on various datasets and how we can leverage it in prediction of health risks. I investigated how well two algorithms—Logistic Regression and Multi-Layered Perceptron (MLP)—predict health outcomes and risk. To be more precise, I evaluated the model's capacity to recognize stroke risk using a dataset of stroke predictions. By means of comparison analysis, this study seeks to clarify the advantages and disadvantages of each algorithm when used with these disparate data kinds, providing information about how well-suited they are for different prediction tasks. Additionally, I provided a framework for data analysis that outlines crucial procedures for data preparation, cleaning, and exploration. This framework may be used to improve the efficacy of machine learning models on a variety of datasets.
Keywords:
Machine Learning Heterogeneous Data; Comparative Analysis; Prediction Modeling; Data Analysis Techniques; Stroke Prediction; Logistic Regression; Multi-Layered Perceptron; Data Preprocessing
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Copyright © 2020 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0