Use of Client-Side Machine Learning Models for Privacy-Preserving Healthcare Predictions - A Deployment Case Study.

Introduction: Machine learning (ML) and deep learning (DL) models in healthcare traditionally rely on server-centric architectures, where sensitive patient data is transmitted to external servers for processing via frameworks like Flask, raising significant privacy concerns. This work demonstrates a privacy-preserving approach by executing healthcare prediction models entirely within the web browser.

Methods: Our approach leverages existing browser-based machine learning and deep learning technologies such as TensorFlow.

ML-CKDP: Machine learning-based chronic kidney disease prediction with smart web application.

Chronic kidney diseases (CKDs) are a significant public health issue with potential for severe complications such as hypertension, anemia, and renal failure. Timely diagnosis is crucial for effective management. Leveraging machine learning within healthcare offers promising advancements in predictive diagnostics.

A Grid Search-Based Multilayer Dynamic Ensemble System to Identify DNA N4-Methylcytosine Using Deep Learning Approach.

DNA (Deoxyribonucleic Acid) N4-methylcytosine (4mC), a kind of epigenetic modification of DNA, is important for modifying gene functions, such as protein interactions, conformation, and stability in DNA, as well as for the control of gene expression throughout cell development and genomic imprinting. This simply plays a crucial role in the restriction-modification system. To further understand the function and regulation mechanism of 4mC, it is essential to precisely locate the 4mC site and detect its chromosomal distribution.