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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Published on: January 11, 2020

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

Senthil Kumar G¹, Dhanagopal R²

¹Department of Computer Science and Business Systems, Chennai Institute of Technology, Chennai, India. senthilkumarg@citchennai.net.

Scientific Reports

|March 13, 2026

Summary

A new deep learning model, TCBiNet, accurately predicts dementia progression using sequential patient data. This temporal convolutional bidirectional attention network improves early detection and clinical relevance for proactive dementia care.

Keywords:

Bidirectional LSTM Early dementia diagnosis Longitudinal health records Neurodegenerative disease prediction Sequential data analysis Temporal convolutional network Temporal deep learning

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Area of Science:

Neuroscience
Artificial Intelligence
Medical Informatics

Background:

Dementia presents a growing global health challenge due to subtle onset and limited temporal diagnostic models.
Traditional machine learning models struggle with static data, failing to capture progressive cognitive decline in patient trajectories.

Purpose of the Study:

To introduce TCBiNet, a novel deep learning framework for modeling short- and long-term dementia symptom evolution using sequential clinical data.
To improve the accuracy and clinical relevance of dementia prediction by capturing temporal patterns.

Main Methods:

Developed TCBiNet (Temporal Convolutional Bidirectional Attention Network), integrating TCN, BiLSTM, and Temporal Attention mechanisms.
Utilized longitudinal data from 2,149 patients (aged 60-90), with metrics segmented into 30-day intervals.
Implemented the framework in Python using TensorFlow 2.11 for sequence-aware analysis.

Main Results:

TCBiNet achieved superior performance compared to conventional models (CNN-LSTM, BiLSTM-DRL).
Achieved 99.51% accuracy, 99.35 F1-score, and 0.990 AUC-ROC, significantly outperforming existing methods.
Demonstrated enhanced interpretability and clinical relevance through temporal pattern mining and attention weighting.

Conclusions:

TCBiNet offers a robust, sequence-aware diagnostic tool for dementia, improving prediction accuracy.
The model facilitates proactive interventions in dementia care through early and relevant detection.
Highlights the potential of longitudinal neurocognitive modeling for early disease identification.