Citation:

Sarika Mulukuntla, Mounika Gaddam, 2021. "Data-Driven Healthcare: Trends in Machine Learning and AI for Disease Prediction and Prevention" ESP Journal of Engineering & Technology Advancements  1(1): 25-33.

Abstract:

In the rapidly evolving world of healthcare, the integration of machine learning and artificial intelligence (AI) has marked a transformative era, especially in the realm of disease prediction and prevention. This movement is not just about technological advancement; it’s about a profound shift towards a more data-driven approach in medicine that promises to enhance patient outcomes, reduce costs, and improve overall health system efficiency. Today, AI and machine learning algorithms are increasingly being harnessed to sift through vast amounts of health data, from electronic health records to genetic information, enabling early detection of diseases such as cancer, heart disease, and diabetes. These technologies are also paving the way for personalized medicine, where treatments and prevention strategies are tailored to the individual, taking into account their unique genetic makeup and lifestyle. Furthermore, predictive analytics in healthcare is empowering professionals to identify at-risk populations and intervene earlier than ever before, thereby preventing diseases from developing or progressing. This trend signifies a hopeful future where healthcare is not just reactive but preventive and treatments are not one-size-fits-all but customized. As we continue to unlock the potential of AI and machine learning in healthcare, we stand on the cusp of a revolution that could redefine patient care and disease management for generations to come.

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Keywords:

Healthcare, Machine Learning, Artificial Intelligence, Disease Prediction.