Proactive Cancer Prediction Using IoT and Deep Learning Before Symptoms

Abstract

The ability to predict cancer before the onset of clinical symptoms represents a paradigm shift in oncology and preventive medicine. Existing diagnostic approaches remain reactive, relying on imaging or symptomatic manifestations that frequently detect the disease only at advanced stages, particularly in pancreatic, lung, and ovarian cancers. To address this gap, we propose a novel methodology that integrates the Internet of Things (IoT), Artificial Intelligence (AI), and Deep Learning for proactive cancer prediction. Continuous high-resolution physiological, behavioral, and environmental data are collected through IoT-enabled wearable and implantable devices and analyzed using a hybrid architecture that combines Autoencoders, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs), with a specific focus on Long Short-Term Memory (LSTM) models. Unlike previous work, which primarily targeted general IoT-based monitoring or symptom-driven detection, this study explicitly demonstrates how the fusion of multidimensional IoT data and advanced deep learning enables the identification of micro-level deviations from an individual’s baseline as early biomarkers of cancer risk. Experiments conducted on synthetic datasets simulating pancreatic, lung, and ovarian cancer progression show that the proposed framework achieves an accuracy of 89%, a sensitivity of 85%, a specificity of 91%, and an AUC of 0.93, with an average early detection lead time of 7.5 months. These findings highlight the rigor and originality of the proposed approach, which advances the field by offering a validated, proactive methodology for cancer prediction and establishing clear differences from prior studies by the authors that focused on narrower IoT applications. This work paves the way for predictive and preventive oncology, where intervention can occur long before clinical manifestation of the disease.