Investigating the use of machine learning in predicting disease outbreaks.
Table Of Contents
Table of Contents:
1. Introduction
1.1 Background
1.2 Objectives
1.3 Scope
2. Literature Review
2.1 Disease outbreak prediction methods
2.2 Machine learning algorithms for disease prediction
2.3 Data sources and feature selection
3. Methodology
3.1 Data collection and preprocessing
3.2 Machine learning model selection and training
3.3 Evaluation metrics and performance analysis
4. Implementation
4.1 Data acquisition and integration
4.2 Feature engineering and selection
4.3 Machine learning model development
5. Results and Analysis
5.1 Performance evaluation of predictive models
5.2 Comparison with traditional disease surveillance methods
Thesis Abstract
This project aims to investigate the use of machine learning techniques in predicting disease outbreaks. Machine learning algorithms have the potential to analyze large volumes of data and identify patterns that can help predict the occurrence and spread of infectious diseases. This research will involve collecting and analyzing epidemiological data, environmental data, and social media data to develop predictive models. The project will focus on evaluating the accuracy and reliability of machine learning algorithms in forecasting disease outbreaks. The findings of this research will contribute to improving early warning systems and enhancing public health preparedness for disease outbreaks.
Thesis Overview
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