Predicting Patient Disease Progression with Cloud-based Decision Trees and IoT Data Integration

Abstract

To predict how a patient’s disease will progress, this research focuses at how effectively cloud-based decision trees (DTs) combined with Internet of Things (IoT) data integration work. The capacity to use these data streams for proactive healthcare treatments is expanding rapidly with the spread of IoT devices that can monitor different physiological markers. It provides an approach that uses cloud-based DTs models in conjunction with real-time data from IoT devices to build illness progression prediction models. Utilizing a varied dataset that covers a range of patient demographics and medical conditions, this method successfully predicts disease trajectories. Cloud computing and IoT can transform healthcare analytics. This might allow doctors to better predict how a patient’s sickness will proceed. Improving patient outcomes while decreasing healthcare expenditures may be possible with this proactive strategy that allows for early interventions. The goal of this research is to predict disease development utilizing IoT-enabled data collecting and cloudbased DTs, resulting in more personalized treatments and better healthcare outcomes with real-time monitoring. Cloud computing and IoT data integration have the potential to enhance predictive healthcare analytics and optimize patient care methods. This novel approaches to illness prediction and management in the age of revolutionary digital healthcare.