###### Abstract  
(1) Background: Lung cancers are the most common cancers worldwide, and prostate cancers are among the second in terms of the frequency of cancers diagnosed in men. Automatic ranking of the risk groups of such diseases is highly in demand, but the clinical practice has shown us that, for a sensitive screening of the clinical parameters using an artificial intelligence system, a customarily defined deep neural network classifier is not sufficient given the usually small size of medical datasets. (2) Methods: In this paper, we propose a new management method of cancer risk groups based on a supervised neural network model that is further enhanced by using a features attention mechanism in order to boost its level of accuracy. For the analysis of each clinical parameter, we used local interpretable model-agnostic explanations, which is a post hoc model-agnostic technique that outlines feature importance. After that, we applied the feature-attention mechanism in order to obtain a higher weight after training. We tested the method on two datasets, one for binary-class in cases of thoracic cancer and one for multi-class classification in cases of unrological cancer, to demonstrate the wide availability and versatility of the method. (3) Results: The accuracy levels of the models trained in this way reached values of more than 80% for both clinical tasks. (4) Conclusions: Our experiments demonstrate that, by using explainability results as feedback signals in conjunction with the attention mechanism, we were able to increase the accuracy of the base model by more than 20% on small medical datasets, reaching a critical threshold for providing recommendations based on the collected clinical parameters. deep supervised learning; thoracic dataset; urology dataset; explainability; attention mechanism; cancer risk groups +
Footnote †: journal: Medical  
herein, we propose a method to improve the management of cancer risk groups by coupling a features attention mechanism with a deep supervised neural network. The attention mechanism is a key component of many modern machine learning algorithms, including neural machine translation, image captioning, and speech recognition [3]. While attention mechanisms have primarily been used in natural language processing and computer vision tasks, recent research has shown their effectiveness in improving the performances of models on tabular datasets as well. In this context, attention mechanisms can be used to help models selectively focus on the most important features of the input data. This can be particularly useful in cases where the input data contains a large number of features or when some features may be more important than others for making accurate predictions. It grants the models the ability to concentrate on specific aspects of input data by assigning degrees of importance to different elements. This mechanism functions through a series of transformations that convert input into query, key, and value vectors. Queries specify the components to focus on, keys identify the elements within the input, and values contain relevant information for retrieval or amplification. Attention scores are computed to determine the relative significance of queries with respect to keys, and these scores are subsequently converted into attention weights through a SoftMax function. The ultimate outcome is achieved by computing a weighted sum of values based on these attention weights. This dynamic approach empowers models to capture intricate dependencies and context-aware information within input sequences, proving to be highly beneficial in tasks such as comprehending natural language, analyzing images in computer vision, and facilitating datasets that are difficult to analyze. To design the deep neural network model, we used layers of the Dense and BatchNormalization types [4]. A dense layer is often used to compute the attention scores.