Approaches performed based on computer supported systems within the medical field gain more popularity day by day. In such systems, Artificial Intelligence techniques are often used for several disease diagnostics. Diabetes is one of these diseases. In this study, a diabetes diagnosis system based on Support Vector Machines has been proposed. Along training of SVM, Vortex Optimization Algorithm was used for determining the sigma parameter of the Gauss (RBF) kernel function, and a classification process has been done over the diabetes data set related to Pima Indians.

Diabetes determination via vortex optimization algorithm based support vector machines

The definition, diagnosis and classification of Diabetes Mellitus and its complications are very important. First of all, the World Health Organization (WHO) and other societies, as well as scientists have done lots of studies regarding this subject. One of the most important research interests of this subject is the computer supported decision systems for diagnosing diabetes. In such systems, Artificial Intelligence techniques are often used for several disease diagnostics to streamline the diagnostic process in daily routine and avoid misdiagnosis. In this study, a diabetes diagnosis system, which is formed via both Support Vector Machines (SVM) and Cognitive Development Optimization Algorithm (CoDOA) has been proposed. Along the training of SVM, CoDOA was used for determining the sigma parameter of the Gauss (RBF) kernel function, and eventually, a classification process was made over the diabetes data set, which is related to Pima Indians. The proposed approach offers an alternative solution to the field of Artificial Intelligence-based diabetes diagnosis, and contributes to the related literature on diagnosis processes.

/pdf/cognitive-development-optimization-algorithm-based-support-5dxhje5cz1.pdf

Cognitive Development Optimization Algorithm Based Support Vector Machines for Determining Diabetes

Perceptions of the adoption of Clinical Decision Support Systems in the Saudi Healthcare Sector

In assessing the benefits of using e-health systems, the main goal of this study is to evaluate the real use of the clinical decision support system (CDSS) between 2007 and 2014 in Canada’s healthc...

Managing patients’ data with clinical decision support systems: a factual assessment

Clinical decision support systems (CDSS) are growing as an important tool in healthcare industry. The literature review indicates there are both benefits and challenges for CDSS implementation and notes the influencing factors that encourage their acceptance. Furthermore, the importance of their effectiveness and the area of improvements in the CDSS implementation are well recognized in the healthcare sector as revealed throughout the literature review. In this paper, we present a theoretical analysis collecting evidence from the existing literature to focus on the issues and requirements of implementing a CDSS in Saudi Arabia. This paper aims to illustrate a broad understanding of CDSS in Saudi Arabia to assess the impact of a successful introduction of CDSS for General Practitioners (GPs).

Acceptance of a Clinical Decision Support System for improving Healthcare Services in Saudi Arabia

Artificial intelligence applications in medicine is the major and evolutionary topic in the technology world. Neural networks is an important branch of machine learning which is inspired from biological neural networks. Neural networks are useful in making proper decisions in rational environments with uncertainty. The neural networks perform better computation with high power with the help of the multiple interconnected neurons which act as processing elements. Decision theory along with probabilistic theory gives the good way to make the right decisions. Neural Network systems help in linking the health observations with the health knowledge database to take better decisions for good health. The ability of a neural network to learn by example can be implemented for taking decisions that would increase the rate of providing the better medical care facilities. This paper presents a novel methodology to implement supervised learning networks that is perceptron networks in medical diagnosis for providing such good decisions to the doctors in helping patients by also providing a good health care.

https://research.ijcaonline.org/volume113/number4/pxc3901609.pdf

Gopichand G

Papers

Application of Perceptron Networks in Recommending Medical Diagnosis