“DiagnoMe” Mobile Application for Identifying and Predicting the Chronic Diseases
2023 5th International Conference on Advancements in Computing (ICAC)(2023)
摘要
This research introduces a novel approach to chronic disease prediction and identification u sing advanced machine-learning techniques. It addresses the challenges posed by environmental conditions and lifestyle habits contributing to chronic diseases, emphasizing the importance of early disease identification f or prevention a nd accurate diagnosis. Common chronic diseases like Parkinson's Disease, Cardiovascular Disease, Pneumonia, and Skin Diseases are discussed as requiring lifelong treatments. The study's four key components include an RNN-based model for predicting Parkinson's disease, a comparative study of prediction methods for cardiovascular diseases, a Neural Network-based technique for automated skin disease detection, and an explanation of Automated Chest X-ray Diagnosis through Advanced Deep Learning. These components collectively offer a comprehensive and innovative approach to chronic disease diagnosis, with the goal of developing a mobile application based on highly accurate deep learning models to improve healthcare outcomes.
更多查看译文
关键词
Chronic Disease Prediction,Machine Learning Models,Mobile Healthcare Application,Cardiovascular Disease,Neural Network-based Segmentation,Vgg19,Xception,Transformer,Evolutionary Computing,Quantization Aware Training,RNN
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要