The use of decision support systems  in health provides improvement in terms of the quality of health and care, early diagnosis of diseases, prevention of person related errors, lowering costs and providing the patients with the optimum treatment.
Today, most physicians prefer the decision support systems in the interest of the effective operation and early determination of the most appropriate option in order to avoid the growing problem related to the management of medical information. The decision support systems are the most ideal aids in either detection or treatment of a disease, or determination of the most appropriate drug.
A basic requirement of our survival is to take a sufficient amount of air and to transfer it to all our cells. While heart and blood cells are responsible for distribution, our lungs perform the exchange of oxygen (O
2) and carbon dioxide (C O
2) by holding a medietary position between the atmosphere and the internal organs. The delivery of oxygen and removal of carbon dioxide, which are the two most important components of the inspiration - expiration process is, called “breathing” . The respiratory function is autonomously carried out in healthy living beings and is artificially carried out in those with lung disease and respiratory failure. The device that is used in this process is called as the ventilator device  and the process is called ventilation.
The physiological parameters determined in accordance with the findings of the patient who is connected to the ventilator device change simultaneously depending on the instantaneous status of the patient after the device has been inserted. The most critical point here is to determine the device parameters in the desired accuracy. Since the task of specifying the parameters of ventilation equipment is carried out entirely by a physician, the physician’s knowledge and experience in the selection of these settings have a direct effect on the accuracy of his/her decisions. The process of determination of the device parameters with a decision support system to be developed for this decision process can be completed with minimum error by predicating the instantaneous status of the patient. By this way, the decision making processes of physicians can be implemented in a faster and a more accurate manner.
This study describes a decision support system designed to automatically determine the ventilator settings in consideration of the ventilation data obtained from two different hospitals. For the model system in our study, COPD (Chronic Obstructive Pulmonary Disease) and CVD (Cerebrovascular Disease) have been used as respiratory diseases. COPD  is a disease that causes the obstruction of the bronchi on the long term and that has no treatment. ARDS  is the acute respiratory distress syndrome caused by the increase in the alveolar capillary permeability. CVD  is a group of disorders with symptoms related to the damaged brain region associated with the blockage or bleeding of the arteries supplying the brain. Although CVD is not a respiratory disease, almost all CVD patients are treated with ventilation devices.
According to the Turkish Statistical Institute’s report of causes of death statistics , 16.311 patients died of disease caused by COPD (J40-J44) and 30.103 patients died of disease caused by CVD (I60-I69) in 2009. In total mortality of 2009, it corresponds to 16,5%.
For this reason, the management of mechanical ventilation devices used in intensive care units is a critical process. Our goal is to minimize errors in this process and prevent deaths caused by incorrect configuration of ventilation devices. This system is designed for the facilities without having lung mechanics like cottage hospitals in country regions. Metropolitan cities have fully equipped hospitals with experienced medical personnel, but on the other hand less experienced doctors can benefit from this system. Our proposed method is facilitated upon making the medical data of patients meaningful with help of an Artificial Neural Network (ANN) , and the determination of the preferred treatment factors.
Below, further studies conducted so far with regards to the subject of our study have been summarized.
In the study of Adeney, Ennett, Frize and Korenberg , this methodology is used and a model using two-stage ANN is presented. It is observed from the test results that a high success rate is provided with regards to the estimation of the parameters.
A similar study has simultaneously performed the inspiration – expiration of the intensive care patients by a ventilator device . The model working in accordance with the data such as the patient’s breathing frequency, the density of oxygen in their blood and the flow wavelength of the blood has estimated the ideal working time.
Borrello’s study  is a detailed modeling of a prototype which is thought as a replacement for the standard ventilator devices and which provides assistance for the operator with regards to the parameters to be selected through its own processor and sensor.
In the study of Tehrani , a closed-loop decision support system that foresees the leaving time of the patient from the ventilator depending on values P C O
2 (Partial Pressure of Carbon Dioxide), P S O
2 (Partial Pressure of Sulfur Dioxide), F i O
2 (Fraction of Inspired Oxygen), PEEP (Positive End-Expiratory Pressure) parameters was proposed.
In another study , simulations have been implemented in the MATLAB environment by using the hybrid algorithm for the ventilators used in intensive care units. With the designed model, the settings regarding the parameters of the blood gasses like F i O
2, PEEP, Pinsp, Vrate are recommended to the physician by the system.
Another computer aided ventilator prototype study  was developed by Ahmedi and Bates. Prototype ventilator opens and closes the valve by the help of the computer program, which sustains the process of inspiration and expiration.
Zhu  and Allerød  proposed ventilation settings with a decision support system according to the patient’s physiological characteristics of the patient to be undergone ventilation, by using ANNs. While Zhu has used the data from 28 ARDS patients in his study; Allerød used the data from 20 CABG patients. In both studies, it is shown that patient characteristics are important in the selection of the ventilator settings.
As a result, it is seen in the research articles  related to the methodology of the ventilator devices controlled by the smart control techniques that the influence of the decision support systems on the decisions of the physician have been quite successful.
Er, Yumusak and Temurtas  presented a comparative chest diagnosis; for chronic obstructive pulmonary, pneumonia, asthma, tuberculosis and lung cancer diseases which was realized by using multilayer, probabilistic, learning vector optimization, and generalized regression.
Gil et al.  used ANN models as tools for support in the medical diagnosis of urological dysfunctions. They developed two types of unsupervised and one supervised neural network to distinguish and classify between ill and healthy patients.
Ushida et al.  used fuzzy neural network analysis (FNN) of health check-up data to provide a personalized novel diagnostic and therapeutic method involving the y-GTP level and the WBC count. They performed a logistic regression analysis, including adjustment to ensure FNN analysis was statistically reliable.
Heckerling et al.  used ANN coupled with genetic algorithms to evolve combinations of clinical variables optimized for predicting urinary tract infection for women. Their method revealed that parsimonious variable sets accurate for predicting urinary tract infection, and novel relationships between symptoms, urinalysis findings, and infection.
The Faculty of Medicine, the Electronic and Computing Engineering Department of the Federal University of Rio de Janeiro (UFRJ) and the Electrical Engineering Department of the Federal Center of Technological Education (CEFET-RJ) worked on a collaborative research project to develop a decision support system for smear negative pulmonary tuberculosis (SNPT) . This project aims to develop, through a multi-disciplinary, multi-institutional, innovative and cost-effective approach, new paradigms to prevent the disease progression and support the rapid evaluation of new therapies.
Moein, Monadjemia and Moallem , investigated typical disease diagnoses using ANNs. After selecting some symptoms of eight different diseases, MLP neural network was used with a fuzzy approach to get more accurate results.
Kamruzzaman and Islam , proposed an algorithm, called rule extraction from ANNs (REANN), to extract rules from trained ANNs for medical diagnosis conditions like breast cancer, diabetes and lenses problem.
Lisboa and Taktak  conducted to assess the benefit of ANNs as decision making tools in the field of cancer. Their study describes the work being done in this area over the last decade.
Clinical applications of ANNs provide benefits in many areas, and there are still many more perspectives to be examined like ethics and clinical prospects .
In the Methods Section, the methodology and design elements of the decision support system are detailed. Experimental results and conclusion are given in Section “Results and discussion” and Section “Conclusions”, respectively.