Malaria Disease Prediction with Ensemble Learning Technique

What is it about?

The use of machine learning will continue to emerge, especially in the field of automated diagnostics and forecasting, because malaria is a major public health problem worldwide, and disease control requires rapid and accurate diagnosis. According to the World Health Organization by 2021, an estimated 235 million people worldwide have been diagnosed with malaria in Southeast Asia, including India, Bangladesh, and Belarus. Malaria is a major problem in tropical and subtropical countries. Therefore, it is necessary to develop a mechanism for early detection of the plasmodium parasite, which is infections. In the conventional method, which is used to diagnose malaria in the laboratory by capturing cells with a microscope, a series of tests are performed and the results are analyzed. In this paper discussed the random forest classifier for malaria detection. First, select the random samples from a given database. Next, algorithm will create a decision tree for each sample and prediction effect on all decision trees. The final result will be generated from the prediction result which is based on voting. This paper strongly recommend that you complete the development of a model to improve the diagnosis of malaria using machine learning techniques.

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Why is it important?

With an estimated 235 million people diagnosed with malaria worldwide, particularly in Southeast Asia and tropical/subtropical regions, malaria represents a significant public health challenge. Rapid and accurate diagnosis is crucial for effective disease control and management. Traditional laboratory-based methods for malaria diagnosis, relying on microscopic examination of blood samples, are labor-intensive, time-consuming, and prone to human error. This underscores the need for more efficient and reliable diagnostic approaches.

Perspectives

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Malaria remains a significant global health concern, particularly in regions like Southeast Asia, where millions of individuals are diagnosed annually. The urgency to address malaria stems from its widespread prevalence and the detrimental impact it has on public health and socio-economic development. Effective disease control necessitates timely and accurate diagnosis. Conventional diagnostic methods, such as microscopic examination, are labor-intensive and prone to errors, leading to delays in treatment and increased morbidity and mortality rates. Therefore, there is a critical need to enhance diagnostic capabilities to expedite intervention measures.

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