## 4 Possible Applications in the Medical Field  
The main application of our tool in medical research is probably in the search for robust non-linear relationships between genetic, environmental or lifestyle factors and the development of a disease or cancer. The pruning or attribution methods described above could make it possible to highlight the roles of various factors and possible non-linearities, e.g., for phenomena affected by thresholds in inputs. Moreover, most of these factors are modifiable; thus, once a non-linear transfer function between the causes (inputs) and a disease index (target) has been found, we can also study what would happen in different scenarios that are possibly conceivable for the future in the case of changes in lifestyles or, for example, in different pollution or climate scenarios. Another case where our tool could be very useful is in the study of drug-disease interaction, especially in the very critical cases of cancer treatments, the success of which is often measured in the years of survival after surgery. Cases observed in patients with certain physical characteristics and treatments actually given (as inputs) using our tool could lead to reliable reports for the reconstruction of their survival years (the possible target). At this point, numerical experimentation using NNs could be conducted to see which treatments (and doses) are preferable for other patients with different characteristics in the input data, in order to increase their survival time. This can be done by the application of our forecasting strategy. In general, neural modelling can analyse complex interactions between genetic, environmental and lifestyle factors to predict an individual's cancer risk and estimate their prognosis. These models can capture non-linear relationships that traditional statistical methods may miss. At present, particular attention is paid to deep learning and its contribution to cancer research; see, for instance [24]. Here, however, we would like to briefly cite some research sectors in which our neural approach to small datasets can benefit cancer studies. Neural modelling can assist in personalising cancer treatment by analysing patient-specific data, including genetic profiles and treatment responses. This approach can help to identify the most effective treatment strategies for individuals, for instance, in terms of personalised chemotherapy selection (see [25] for a problem of this type). Neural networks can also analyse non-linear interactions within the tumour microenvironment, including interactions between cancer cells, immune cells and the extracellular matrix. This can shed light on tumour behaviour and immune responses, an important relationship which needs to be deepened; see [26] for a recent review on this topic. Neural modelling can predict the non-linear responses of cancer cells to various drugs. Considering the complexity of drug-target interactions, these models can optimise drug selection for individual patients, including in cases of the use of \"non-oncology drugs\", such as in [27]. Another field in which neural modelling can be very useful is that of non-linear resistance mechanisms. For instance, our tool can help uncover non-linear resistance mechanisms affecting cancer therapies, including targeted therapies and immunotherapies, by considering the interplay between tumour cells and the immune system (see [28] for a review on this important topic). Finally, our neural tool performs multiple non-linear regressions, but with a little modification, it could be adapted for classification problems. In this case, it could also help with diagnostic analyses (inputs) and their use for the early detection of cancer (the target). In particular, if databases that are not too small are available, the linear and non-linear influences of certain inputs on the target could be identified in the analysis values. And then, along with understanding the role of individual inputs, one could also apply the 'law' found to cases of new patients, identifying the eventual onset of cancer.