Predictive modeling for early diagnosis of dementia using sequential data analysis and data mining

Dementia, a progressively debilitating neurodegenerative disorder, poses a growing challenge to global healthcare due to its subtle early onset and lack of accurate temporal diagnostic models. Traditional machine learning (ML) approaches for dementia prediction, including SVMs, Random Forests, and CNNs, often rely on static, cross-sectional data that fail to capture the progressive cognitive decline observed in real-world patient trajectories. To address these limitations, this study introduces a novel deep learning-based framework TCBiNet (Temporal Convolutional Bidirectional Attention Network) designed to model both short- and long-term symptom evolution using sequential clinical data. The architecture strategically integrates Temporal Convolutional Networks (TCN) to identify localized trends, BiLSTM for bidirectional temporal analysis, and a Temporal Attention mechanism to highlight critical symptom intervals. Implemented in Python using TensorFlow 2.11, the model processes longitudinal data from 2,149 patients aged 60-90, with cognitive, behavioral, and functional metrics segmented into 30-day intervals. Compared to conventional models such as CNN-LSTM and BiLSTM-DRL, TCBiNet demonstrated superior performance, achieving an accuracy of 99.51%, F1-score of 99.35, and AUC-ROC of 0.990 reflecting an improvement of over 4.4% in accuracy and 6.2% in F1-score relative to the best-performing existing models. This significant gain is attributed to the model's capacity for temporal pattern mining and attention-guided symptom weighting. The proposed system not only improves prediction accuracy but also enhances interpretability and clinical relevance. By offering a robust, sequence-aware diagnostic tool, this research paves the way for proactive interventions in dementia care, encouraging further exploration into longitudinal neurocognitive modeling for early disease detection.