Enhancing Diagnostic Accuracy: Leveraging Continuous pH Surveillance for Immediate Health Evaluation

Accurate and timely health evaluation is a cornerstone of modern medical care. This paper presents a novel framework that integrates continuous pH surveillance with machine learning techniques to enhance diagnostic precision and responsiveness. Conventional pH assessments are limited by their intermittent nature, often missing transient yet critical physiological fluctuations. In this research, we propose a system combining wearable biosensors and intelligent data analysis to provide minute-level monitoring of pH variations in real time. The system identifies early deviations from individual baselines, potentially indicating conditions like acidosis, sepsis, or renal distress. Utilizing models such as Long Short-Term Memory (LSTM) networks and anomaly detection algorithms, it analyzes patterns to recognize abnormalities. This approach not only improves detection accuracy through continuous analysis but also facilitates predictive diagnostics, enabling proactive medical intervention to prevent further decline. By merging technology with personalized healthcare, this interdisciplinary.