(untitled) (CAHIERS MAGELLANES-NS, 2024)
Abstract-. In the past few years, Machine learning techniques have revolutionized the field of healthcare. Machine learning enable accurate and timely disease prediction, and the integration of machine learning techniques in healthcare has shown remarkable. This unlocks a way to predict multiple diseases simultaneously can significantly improve early diagnosis and treatment. This leading to better patient outcomes and reduced healthcare costs. This paper presents a comprehensive approach for multi-disease prediction using machine learning algorithms. The aim of the model is to predict the likelihood of multiple diseases simultaneously, leveraging various patient data such as demographic information, medical history, and clinical indicators. The study utilizes a diverse dataset comprising electronic health records (EHRs) collected from healthcare institutions. The evaluation of the proposed model demonstrates promising results in terms of prediction accuracy, sensitivity, and specificity across different diseases. The research findings highlight the potential of machine learning in multi-disease prediction and its potential impact on public health. This research paper explores the application of machinelearning algorithms in predicting multiple diseases, focusing on their benefits, challenges and future directions.