IJACIS
Dementia Detection Unit Via A Wearable Internet Of Things Fused With Deep Machine Learning Scheme
Dementia affects over 50 million people globally, with numbers expected to triple by 2050. Traditional diagnostic methods lack early detection capabilities and realtime monitor, leading to delayed intervention and increased caregiver burden. Our study deploys an integrated dementia detection unit combined with wearable Internet of Things (IoT) and deep machine learning model for early identification and continuous monitor of dementia symptoms. A comprehensive system was designed incorporating three components namely: (a) a wearable IoT device using MAX30102 sensor and the ESP32 microcontroller for physiological data collection, (2) a hybrid CNN-LSTM deep learning ensemble, and (c) a mobile app interface for data visualization. The dataset comprised 1,510 patient records with 11 features including demographic, cognitive, and clinical variables. Data preprocessing involved missing value handling, categorical encoding, feature scaling, and SMOTE balancing to address class imbalance. The Multi-Layer Perceptron showed superior performance on the balanced dataset with 0.97 Accuracy, 1.00 Sensitivity, 0.94 Specificity, and 0.98 AUC-ROC. The wearable device successfully collected real-time physiological data including blood glucose, blood pressure, and cholesterol levels – with our app providing seamless integration between hardware and software components, enabling realtime monitor alert and prediction delivery. The integrated system yields a significant advancement in dementia care with early detection abilities, continuous monitor, and improved accessibility. The MLP model's exceptional performance, combined with the practical wearable implementation, provides a scalable solution for healthcare systems seeking to improve dementia diagnosis and management while reducing caregiver burden.