Uterine Cancer Detection and Prevention Using Machine Learning and IoT
The integration of Machine Learning (ML) and the Internet of Things (IoT) can enhance the early detection and prevention of uterine cancer. By using biochemical sensors, this system monitors estrogen and progesterone levels through wearable devices, smart patches, or blood-based tests. The collected data is transmitted to the cloud via microcontrollers like ESP8266, enabling real-time monitoring and analysis.
Machine learning models play a vital role in risk assessment. Convolutional Neural Networks (CNNs) analyze histopathology images, while Random Forest and XGBoost classify patient risk based on symptoms and hormone fluctuations. LSTM networks track hormonal variations over time to identify patterns that may indicate cancer risk. Based on AI-driven predictions, patients are categorized as low, moderate, or high risk, triggering alerts for further screening or medical intervention if necessary.
This system also supports preventive care by providing AI-driven health recommendations, including diet, exercise, and lifestyle changes. A doctor’s dashboard tracks real-time hormone trends, while wearable IoT devices continuously monitor vitals. If abnormal patterns are detected, healthcare providers are notified for early intervention.
By combining ML-based diagnostics and IoT-driven monitoring, this system enables proactive healthcare, allowing for early detection, real-time tracking, and timely medical action. This approach improves patient outcomes, enhances early cancer prediction, and provides a more effective way to manage uterine cancer risks