Healthcare Prediction Based on Machine Learning and Convolutional Neural Network.

Abstract

The integration of Machine Learning (ML) and Convolutional Neural Networks (CNNs) has significantly advanced predictive analytics in healthcare. These technologies enable the analysis of complex medical data, facilitating early diagnosis, personalized treatment, and efficient resource allocation. CNNs, renowned for their prowess in image recognition, have been effectively applied to medical imaging tasks such as tumor detection, diabetic retinopathy classification, and organ segmentation. Simultaneously, ML algorithms, including decision trees and support vector machines, complement CNNs by processing non-image-based medical data, aiding in patient risk assessment and prognosis prediction. Despite these advancements, challenges persist, including data scarcity, class imbalance, model interpretability, and ethical concerns regarding patient privacy. This paper explores the current landscape of ML and CNN applications in healthcare prediction, highlighting their capabilities, limitations, and potential future directions.