Paper Contents
Abstract
This project explores advancements in multiple disease prediction using machine learning techniques, focusing on the integration of different healthcare datasets. As the complexity of diseases often involves multifactorial causes, traditional predictive models may fall short. This study uses advanced algorithms, including various methods to improve accuracy and efficiency in predicting different diseases. Additionally, we analyze the impact of data preprocessing and normalization on model outcomes. Our results demonstrate significant improvements in predictive accuracy compared to conventional methods, highlighting the potential of machine learning to support early diagnosis and personalized treatment strategies in healthcare. This research highlights the importance of advancing medical technology and improving patient outcomes by predicting their diseases at early stage.
Copyright
Copyright © 2024 Shruti Manojkumar Dalvi. This is an open access article distributed under the Creative Commons Attribution License.
