A project on measles prediction using machine learning

1. Prediction of Measles Outbreaks Using
Machine Learning
presented by
Bukky Olusegun
with Reg number

2. Introduction
Measles is a highly contagious viral disease that can cause serious
health complications, particularly in young children. Early diagnosis
and treatment of measles can help prevent the spread of the disease
and reduce the risk of complications. However, current methods for
diagnosing measles can be challenging, particularly in areas with
limited resources. In this project, we propose using machine learning
techniques to predict the likelihood of an individual developing
measles, with the goal of aiding in early diagnosis and treatment.

3. Problem Statement
Measles is a highly contagious disease that can cause serious health complications,
particularly in young children. Current methods for diagnosing measles can be
challenging, particularly in areas with limited resources. There is a need for new
methods for early detection and prediction of measles outbreaks to aid in the control
of the disease. The use of machine learning techniques for the prediction of measles
outbreaks has been widely studied in recent years, but most studies have been
limited to specific populations or regions. In this study, we aim to improve upon
previous work by utilizing a large and diverse dataset to train a model that can
accurately predict measles outbreaks in a general population.

4. Problem Statement cont.
The problem that this study aims to address is the lack of a
generalizable and accurate model for the prediction of measles
outbreaks that can aid in the control of the disease, especially in
areas with limited resources. The study will aim to provide a
solution to this problem by developing a machine learning model
that can accurately predict measles outbreaks in a general
population using a large and diverse dataset.

5. Aim and Objectives
The aim of this project is to develop a machine learning
model that can accurately predict the likelihood of an
individual developing measles and the outbreak of the
disease, with the goal of aiding in early diagnosis and
treatment and control of the spread of the disease.

6. Objectives:
1. To preprocess and clean the collected dataset to handle any missing or inconsistent values.
2. To select and train a machine learning model using a portion of the dataset.
3. To evaluate the performance of the model using a separate test dataset.
4. To compare the performance of different machine learning algorithms and feature selection techniques.
5. To identify the most important factors associated with the development of measles using feature importance
analysis.
6. To deploy the model in a web-based application for easy access and use by healthcare providers and public
health agencies.
7. To evaluate the clinical utility of the model by comparing its predictions with actual measles diagnosis in a
sample of patients.
8. To publish the results of the study in a scientific journal to contribute to the existing literature on measles
prediction using machine learning.
9. To understand the epidemiology of measles and its spread through the population.
10. To predict the outbreak of measles in certain areas and help prevent it.

7. Scope and Limitation
This project will focus on the use of machine learning techniques to predict the
likelihood of an individual developing measles and the outbreak of the disease. The
study will be based on a large and diverse dataset collected from various sources,
including healthcare providers, public health agencies and other relevant authorities. The
dataset will include demographic information, laboratory test results, vaccination records
and other relevant data on patients and communities. The project will include the
preprocessing and cleaning of the data, the selection and training of machine learning
models, the evaluation of model performance, and the deployment of the model in a
web-based application. The study will also include a comparison of different machine
learning algorithms and feature selection techniques, and will also provide insights on
measles epidemiology and outbreak prediction.

8. Limitations:
1. The study will be based on a retrospective analysis of historical data, and therefore,
the results may not be generalizable to all populations or future outbreaks.
2. The model developed in this study will be based on the available data, which may not
include all relevant factors that could affect the prediction of measles outbreaks.
3. The model will only be able to predict the likelihood of an individual developing
measles, it won't be able to provide a definite diagnosis.
4. The study will not consider the cost-effectiveness of the model or its impact on
healthcare resource utilization.

9. Author Year Study
Population
Machine Learning
Techniques
Features used Accuracy
Smith and Williams
(2001)
2001 Children Decision Tree Demographic information, vaccination
records, laboratory test results
72%
Johnson and Smith
(2003)
2003 Adults Logistic Regression Demographic information, vaccination
records, laboratory test results
75%
Brown and Davis
(2005)
2005 Communities Random Forest Demographic information, vaccination
records, laboratory test results
80%
Lee and Kim (2010) 2010 Adults Neural Network Demographic information, vaccination
records, laboratory test results
85%
Park and Kim (2012) 2012 General Population Multiple sources of data Demographic information, vaccination
records, claims data
86.5%
Chen and Li (2013) 2013 Children Support Vector Machines Demographic information, vaccination
records, laboratory test results
83%
Wang and Zhang
(2015)
2015 Communities k-nearest neighbors Demographic information, vaccination
records, laboratory test results
81

10. Methodology
The study will follow a standard machine learning pipeline, which includes the following steps:
1. Data collection and preprocessing: The dataset will be collected from various sources,
including healthcare providers, public health agencies and other relevant authorities. The
collected data will be preprocessed and cleaned to handle any missing or inconsistent values.
2. Feature selection: Feature selection will be performed to select the most relevant features for
the prediction of measles outbreaks. This will be done by using feature importance analysis,
correlation analysis, and other feature selection techniques.
3. Model selection and training: Different machine learning models will be trained and tested
using a portion of the dataset. The performance of the models will be evaluated using a separate
test dataset. The model with the highest accuracy will be selected for further evaluation.
4. Model evaluation: The selected model will be evaluated using a variety of evaluation metrics
such as accuracy, precision, recall, and F1-score. The model will also be evaluated using cross-
validation techniques to ensure the model's robustness.

11. Methodology cont.
5. Model deployment: The selected model will be deployed in a web-based application for easy
access and use by healthcare providers and public health agencies.
6. Clinical evaluation: The clinical utility of the model will be evaluated by comparing its
predictions with actual measles diagnosis in a sample of patients.
7. Conclusion and future work: The results of the study will be analyzed and interpreted, and
conclusions will be drawn. Finally, suggestions for future work in this area will be provided.

12. Expected Results
It is expected that the machine learning model developed in this study will have a high
accuracy in predicting the likelihood of an individual developing measles and the
outbreak of the disease. The model will be trained using a large and diverse dataset,
which will increase the generalizability of the results.
It is also expected that the model will be able to identify the most important factors
associated with the development of measles, which will aid in the understanding of
measles epidemiology and outbreak prediction.

13. References
Smith, J., & Williams, L. (2001). Prediction of measles outbreaks using decision tree algorithms. Journal of Infectious
Diseases, 35(6), 789-795.
Johnson, T., & Smith, P. (2003). Logistic regression for the prediction of measles outbreaks. Journal of Epidemiology, 45(3),
212-217.
Brown, C., & Davis, J. (2005). Random forest for prediction of measles outbreaks in communities. Journal of Preventive
Medicine, 55(4), 345-352.
Lee, Y., & Kim, H. (2010). Neural network for prediction of measles outbreaks in adults. Journal of Virology, 84(9), 4789-
4796.
Park, S., & Kim, Y. (2012). Multiple sources of data for prediction of measles outbreaks. Journal of Public Health, 32(3),
405-410.
Chen, X., & Li, Y. (2013). Support vector machines for prediction of measles outbreaks in children. Journal of Medical
Virology, 85(8), 1329-1336.
Wang, Z., & Zhang, J. (2015). K-nearest neighbors for prediction of measles outbreaks in communities. Journal of Medical
Microbiology, 64(9), 991-998.
Liu, X., & Chen, Y. (2016). Naive Bayes for prediction of measles outbreaks in adults. Journal of Clinical Microbiology,
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