Utilization of Machine Learning in Disease Anticipation and Prevention

Abstract

Predictive and preventative strategies for the disease have been transformed through ML (machine learning), which has created opportunities for earlier diagnosis and personalized care that were not previously available in healthcare. This chapter summarizes the role of ML in healthcare, emphasizing its importance in predicting diseases and preventing their onset. The key algorithms, including decision trees, neural networks, and support vector machines, and the fundamentals of ML (supervised, unsupervised, and reinforcement learning) are covered. It covers different data sources for ML applications, including genomic data, wearables, and public health data. Data preprocessing and feature engineering steps, such as cleaning, selection, and transformation, are also covered. The chapter delves into model training, evaluation metrics, and challenges such as handling imbalanced data, overfitting, and underfitting. It highlights personalized disease prediction models and risk factor assessments, which can show how individual health data can lead to more tailored predictions. The role of ML in preventive healthcare is also explored, with a focus on early intervention approaches and lifestyle change recommendations. It further explains the significant implementation of ML for disease prediction, including early detection of kidney diseases, infectious outbreaks, and mental health disorders. Finally, this chapter also discusses the challenges and limitations of the implementation of ML in healthcare.

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