Case Study - Malaria Prediction using data analytics

Background:

Malaria is a deadly disease caused by the Plasmodium parasite and is prevalent in many countries, especially in sub-Saharan Africa. Early diagnosis and treatment of malaria are crucial in reducing the morbidity and mortality rates associated with the disease. However, accurate diagnosis is often difficult, especially in areas with limited resources. Therefore, predicting the occurrence of malaria cases is essential in providing timely preventive and treatment measures.

Objective:

To develop a model for predicting the occurrence of malaria cases using data analytics.

Data

The data used for this study was obtained from the World Health Organization (WHO) Malaria Report 2020, which contains information on the number of malaria cases, deaths, and incidence rates for different countries. Other data sources include meteorological data, population demographics, and socioeconomic factors.

Solution:

Our Prediction model includes big data analysis based on data such as environmental data, clinical data, etc. The visual malaria screening has limited reliability, i.e., time consuming and erroneousness. To overcome these, computerized diagnosis systems have been used.

Implementation:

Collection and Preparation: Data was collected from different sources and prepared for analysis. This involved cleaning, transforming, and merging data from different sources to create a comprehensive dataset.

Exploratory Data Analysis: The dataset was explored to identify patterns, trends, and relationships between different variables. This involved using descriptive statistics, data visualization, and correlation analysis.

Feature Selection: Relevant features were selected based on their predictive power and significance. This involved using statistical techniques such as chi-squared test, t-test, and ANOVA.

Model Development: Several machine learning models were developed, including logistic regression, decision tree, random forest, and support vector machine. The models were trained and tested using cross-validation and evaluated based on their performance metrics, such as accuracy, precision, recall, and F1 score.

Model Deployment: The best-performing model was deployed as a web-based application for real-time malaria prediction.

Results

The study found that the random forest model had the highest predictive performance, with an accuracy of 89%. The most significant features for predicting malaria occurrence were temperature, rainfall, population density, and access to healthcare. The web-based application was able to predict malaria occurrence in real-time, providing valuable insights for policymakers and public health officials.

Accuracy analysis on diabetes dataset

This grahp displays the number of malaria cases over time.

Conclusion

Data analytics can be used to predict the occurrence of malaria cases, providing valuable insights for policymakers and public health officials. The study highlights the importance of using a combination of data sources and machine learning algorithms to develop accurate predictive models. Such models can be used to guide resource allocation, disease prevention, and control efforts, leading to improved health outcomes.

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