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A disease prediction system plays a vital role in today's healthcare landscape. Accurately predicting a disease remains a complex task due to various influencing factors. By leveraging machine learning, data analytics, and artificial intelligence, these systems assess the likelihood of medical conditions, allowing for timely and informed healthcare decisions. Such predictive approaches support early diagnosis, preventive care, tailored treatment strategies, and can significantly reduce overall medical expenses. The Naïve Bayes classifier is a probabilistic model based on Bayes' theorem, assuming that features are conditionally independent given the class label. Despite this strong assumption, it has demonstrated remarkable performance in medical applications, particularly for disease prediction. The effectiveness of the Naïve Bayes classifier lies in its ability to process medical datasets efficiently while maintaining reasonable classification accuracy. The model is particularly useful when working with small or incomplete datasets, as it requires fewer training samples compared to complex models like deep learning. Additionally, its capability to handle categorical and continuous data makes it a versatile choice for various disease prediction tasks. Among various classification algorithms, the Naïve Bayes classifier is widely used due to its simplicity, efficiency, and ability to handle uncertainty. Naïve Bayes is a widely used machine learning method that proves highly effective in predicting health outcomes, diagnosing medical conditions, and assessing future risks. It has been successfully applied in identifying the likelihood of various diseases such as diabetes, heart disease, kidney disorders, cancer, sleep apnea, and several others, making it a valuable tool in modern medical decision-making. This paper presents a comprehensive analysis of disease prediction systems based on the Naïve Bayes classifier. We discuss its theoretical foundations, advantages, limitations, and applications in diagnosing diseases such as heart disease, diabetes, and cancer..