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In the field of healthcare, machine learning algorithms have shown promise

in predicting diseases such as diabetes, COVID-19 and cardiovascular

disease. Accurate data collection and preprocessing are crucial steps to ensure
the reliability of the models. The use of different algorithms like Logistic

Regression, Random Forest, and AdaBoost allows for comparative analysis
and identification of the most effective approach. The findings underscore the

significance of these techniques in improving patient outcomes through early

detection and intervention.

Additionally, the studies emphasize the importance of user-friendly
interfaces and web-based applications in providing intuitive data

visualization, analysis, and information access. The development of
dashboards and web applications allows users to track disease trends, assess

their risk, and obtain timely predictions. These tools facilitate collaboration

and data-driven decision-making among healthcare professionals and
government bodies, ultimately contributing to better control and management

of problems.

Furthermore, the analysis highlights the role of machine learning algorithms

in tracking and predicting the risk of diseases. By leveraging data from
multiple sources and applying techniques like Random Forest, researchers

can provide real-time visualizations, trend predictions, and access to reliable
information. These tools enable users to better understand the situation, make

informed decisions, and take appropriate preventive measures.

2.10 Summary

This chapter summarized earlier studies about disease prediction using

machine learning. From the investigation, the researcher discovered
numerous relevant pieces of prior research related to the project domains.

These studies provided insights into various aspects, such as the machine
learning algorithms used, their efficacy, and more. Additionally, the

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