Date of Publication :8th August 2024

Abstract:The ability to predict human diseases from symptoms is a pivotal aspect of modern healthcare, revolutionizing the way we approach preventive medicine and patient care. The work presented on title "IMPLEMENTATION OF DISEASE PREDICTION ALGORITHM USING MACHINE LEARNING", delves into the importance and implications of disease prediction using symptoms. By harnessing the power of advanced data analytics, machine learning, and artificial intelligence, healthcare providers and researchers can now develop highly accurate predictive models that enable early disease detection. These models analyse an array of patient data, including symptoms, medical history, genetic information, and environmental factors, to identify potential health risks and predict disease onset. Such predictive capabilities have the potential to save countless lives, reduce healthcare costs, and improve overall quality of life for individuals by enabling timely interventions and personalized treatment plans. This work highlights the challenges and future prospects of disease prediction from symptoms. While significant strides have been made in this field, challenges remain in terms of data privacy, model interpretability, and the need for robust and diverse datasets. In this work, naive bayes has been implemented for disease prediction since it works better on the textual dataset according to best of my knowledge. Disease prediction from symptoms represents a transformative approach to healthcare, paving the way for a proactive and personalized healthcare system that not only treats diseases but also anticipates and prevents them, ultimately improving public health outcomes worldwide.

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