Neural Network and its applications

Neural Networks in medical diagnosis and applications

Introduction and architecture of Neural Networks

It is shown that ANN can be effective both in patterns and trends detection therefore can be successfully implemented in forecasting and predictions. ANN are capable of mapping input data pattern to corresponding output structure. Additionally, they exhibit an ability to recall entire structure out of incomplete and faulty patterns. Feed-forward ANN in comparison to feedback networks are less complex and have signals traveling in one direction only from input to output. Feedback ANN are more powerful and dynamic. In addition, they include feedback loops in signals’ trajectory meaning signals moving both ways between input and output which makes them extremely complicated yet allows more capabilities. Since ANN were proven to be successfully implemented for data pattern recognition, they can be applied in various industries that require forecasts or predicting.

Training process and database for medical diagnosis

One of the core principles of neural networks’ functionality is training process based on a corresponding database. For ANN to undergo learning procedure for medical diagnosis, a sufficient dataset in a form of table or matrix with known diagnosed patients’ condition analysis is required. Medical data including symptoms and laboratory data is used as input for artificial neural network. Training process is continued until a minimum amount of data is processed as a base. The procedure is followed by additional checks and verification. The examples of patients used for the learning should be reliable for ANN generalization to be more precise in forthcoming predictions. Dataset used for final verification should differ from dataset that was initially used for training. Otherwise, verification results should not be considered as valid. Finally, ANN evaluations should be firstly tested in medical practice by a clinician and in case of successful prediction are added to learning database of ANN.

ANN application in diabetes and cancer diagnosis

ANN based diagnostics system for diabetes detection was firstly introduced in 2011. Medical data of 420 patients including rate of change of heart rate and physiological parameters was used as a base both for training and final evaluation verification. Additionally, ANN can be applied to control optimal level of blood glucose and insulin for diabetic patients according to trajectory relevant for healthy individuals without the disease. Within this control system fuzzy logic algorithm is implemented. Implementing ANN for identifying different cancer types or predicting possibility of their further development based on patients’ input data was suggested back in the late 1990s. Being an effective tool for pattern recognition, ANN is suitable both for classifications and clustering. Supervised models of ANN are applied for classification of gene expression and unsupervised models can be implemented for distinguishing a pattern in a set of unlabelled data. Supervised learning requires a teacher to be involved to minimize possible error rate. Training is a very time-consuming process. As a result, according to research most of ANN allow to diagnose various types of cancer at early stage and with proven accuracy. However, the drawback of using this technology remains in extensive training time. Documented accuracy of correct predictions in terms of tumour recurrence rate done by neural networks is 960 out of 1008 cases. Probable recurrence forecast can be made based on the following set if data such as tumour size and hormone receptor status as well as the number of palpable lymphatic nodules. In addition to identification of tumours, scientific research is held to make the application of ANN possible for diagnosis of recently emerged diseases such as Swine Flu, Brain Fever and Chicken Guinea.

Instant Physicians and Electronic Noses

Instant Physician application was under development back in the 1980s and was designed to determine the most probable diagnosis together with the best offered treatment based on the analysis of a set of symptoms used as initial input. Electronic Noses are meant to be implement in tele-present surgery. Identifying a particular smell during an operation can be crucial for a surgeon working remotely therefore it can be done with the help of an Electronic Nose capable of distinguishing odours that can be then electronically channelled from the surgical environment and regenerated for a tele-present surgeon. Additionally, these devices are applied in research and development laboratories.

Neural Networks application in forecasting natural hazards

ANN application in prediction and management of natural disasters

Disaster prediction based on ANN model can be a crucial part of pre-disaster management phase for disasters of climatic origin including flooding or drought. As disastrous flooding can be triggered by various factors such as rainfall, snowmelt, high tidal waves or failure of the river blockages, sufficient dataset on areas prone to flooding is required for successful forecasts. Data from the base sensors including temperature, humidity, rain fall, wind speed and under ground water level is used as input. ANN is previously trained with the use of selected training database consisting of flooding occurrences and non-occurrences. ANN forecasts probability of either occurrence or non-occurrence of flooding by processing the climatic data of examined area as an input. Test evaluation results are statistically precise.

Prediction of natural hazards with Neural Networks

One of the cornerstones of preventive measures against natural hazards are hazard zone maps. According to the colour coding vulnerable habitable areas are marked in yellow and highly vulnerable areas are marked in red accordingly. This model was firstly introduced in 1975 in Austria. The process of creating hazard zone maps is complicated and expensive. For this reason, there are many areas and regions for which detailed information is not there. Artificial Neural Networks can be used to learn from already existing hazard zone maps and consequently generate them for other areas based on the previously gained knowledge. One of the drawbacks of this process is required amount of training dataset needed for sufficient and successful outcome of the training. Learning methods can be optimized with the application of unsupervised learning model hence allow smaller sets of training data while keeping up the required quality of the results. Currently most scientific studies of natural disasters focus on a single hazard at a time during the research. This approach does not take into consideration relationships and co-dependency of multiple hazards therefore might inevitably lead to miscalculations of possible risks. For this reason, ANN being a powerful tool for analysis of large datasets, is suitable for approaches observing multi-hazard relationships and documented information about interactions between several extreme natural events and considering individual as well as collective risks triggered by several hazards.

Prediction of seismic hazards with the use of ANN

Earthquake events are dependent on multiple variables to be considered for the efficient forecasting process. Among the key input values for further analysis is ground motion data. One of the recent studies, implementing machine learning for earthquake predictions, was held in Chile and included the use of immense database of 86 thousand seismic events’ records that took place in Chile between 2000 and 2017. The results of the experiment are considered satisfactory for successful predictions of seismic events in the future.