ABSTRACT
This paper examines how the increasing prevalence of chronic diseases can be mitigated through the application of machine learning innovations. Our system employs advanced technologies like Convolutional Neural Networks (CNNs) for extracting features and K-Nearest Neighbors (KNNs) for predicting diseases. It integrates lifestyle data, medical history, and patient symptoms to automatically detect diseases at an early stage and offer personalized prognoses. This system, enhanced with LLM (Long Short-Term Memory), aims to surpass traditional methods by analyzing large datasets and identifying subtle patterns. It offers physicians valuable insights that enable timely intervention and improved preventive healthcare. Comparative research evaluating the system's performance against alternative algorithms such as Naïve Bayes, Decision Trees, and Logistic Regression reveals its potential to greatly enhance the field of artificial intelligence in safeguarding human health.
