Building a Machine Learning Model for Asthma Prediction

Abstract

Asthma is a unique disease because while it can be life-threatening, it is entirely manageable with the right precautions.Yet, in developing countries like India, the death rate due to asthma is alarmingly high, even though it could be minimized by using inhaled corticosteroids and nebulizers.This is primarily due to the lack of facilities and qualified healthcare professionals in these countries.Today, India is a country of approximately 1,296,667,068 people (as of this writing), presenting an enormous diversity and, therefore, an enormous challenge to the healthcare delivery system.One way to tackle this problem is to leverage advanced machine learning (ML) technology and rely on an ML model to assist in making the diagnosis.In this paper, we demonstrate how such an ML model can be built, discuss data cleaning and preprocessing techniques that need to be employed, and compare different modeling approaches such as Logistic Regression, Gradient Boosting, and Random Forest.We conclude that while this direction is promising, the current lack of high-quality datasets is a barrier to achieving diagnostic accuracy comparable to that of experts.To facilitate further research on this topic, we have also made a Jupyter notebook, along with the results discussed in this paper, available as open-source software on GitHub.

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