  Existing learning models often utilise CT-scan images to predict lung
diseases. These models are posed by high uncertainties that affect lung
segmentation and visual feature learning. We introduce MARL, a novel Multimodal
Attentional Representation Learning model architecture that learns useful
features from multimodal data under uncertainty. We feed the proposed model
with both the lung CT-scan images and their perspective historical patients'
biological records collected over times. Such rich data offers to analyse both
spatial and temporal aspects of the disease. MARL employs Fuzzy-based image
spatial segmentation to overcome uncertainties in CT-scan images. We then
utilise a pre-trained Convolutional Neural Network (CNN) to learn visual
representation vectors from images. We augment patients' data with statistical
features from the segmented images. We develop a Long Short-Term Memory (LSTM)
network to represent the augmented data and learn sequential patterns of
disease progressions. Finally, we inject both CNN and LSTM feature vectors to
an attention layer to help focus on the best learning features. We evaluated
MARL on regression of lung disease progression and status classification. MARL
outperforms state-of-the-art CNN architectures, such as EfficientNet and
DenseNet, and baseline prediction models. It achieves a 91% R^2 score, which is
higher than the other models by a range of 8% to 27%. Also, MARL achieves 97%
and 92% accuracy for binary and multi-class classification, respectively. MARL
improves the accuracy of state-of-the-art CNN models with a range of 19% to
57%. The results show that combining spatial and sequential temporal features
produces better discriminative feature.
