A survey on utilization of data mining approaches for dermatological (skin) diseases prediction

Due to recent technology advances, large volumes of medical data is obtained. These data contain valuable information. Therefore data mining techniques can be used to extract useful patterns. This paper is intended to introduce data mining and its various techniques and a survey of the available literature on medical data mining. We emphasize mainly on the application of data mining on skin diseases. A categorization has been provided based on the different data mining techniques. The utility of the various data mining methodologies is highlighted. Generally association mining is suitable for extracting rules. It has been used especially in cancer diagnosis. Classification is a robust method in medical mining. In this paper, we have summarized the different uses of classification in dermatology. It is one of the most important methods for diagnosis of erythemato-squamous diseases. There are different methods like Neural Networks, Genetic Algorithms and fuzzy classifiaction in this topic. Clustering is a useful method in medical images mining. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. Clustering has some applications in dermatology. Besides introducing different mining methods, we have investigated some challenges which exist in mining skin data

Diabetes Prediction Using Artificial Neural Network

Diabetes is one of the most common diseases worldwide where a cure is not found for it yet. Annually it cost a lot of money to care for people with diabetes. Thus the most important issue is the prediction to be very accurate and to use a reliable method for that. One of these methods is using artificial intelligence systems and in particular is the use of Artificial Neural Networks (ANN). So in this paper, we used artificial neural networks to predict whether a person is diabetic or not. The criterion was to minimize the error function in neural network training using a neural network model. After training the ANN model, the average error function of the neural network was equal to 0.01 and the accuracy of the prediction of whether a person is diabetics or not was 87.3

Rule Based System for Diagnosing Wireless Connection Problems Using SL5 Object

There is an increase in the use of in-door wireless networking solutions via Wi-Fi and this increase infiltrated and utilized Wi-Fi enable devices, as well as smart mobiles, games consoles, security systems, tablet PCs and smart TVs. Thus the demand on Wi-Fi connections increased rapidly. Rule Based System is an essential method in helping using the human expertise in many challenging fields. In this paper, a Rule Based System was designed and developed for diagnosing the wireless connection problems and attain a precise decision about the cause of the problem. SL5 Object expert system language was used in developing the rule based system. An Evaluation of the rule based system was carried out to test its accuracy and the results were promising

Fuzzy Expert System for Tropical Infectious Disease by Certainty Factor

Communication between doctor and patient play an important role in determining the diagnosis of the illness suffered by the patient. Consultation time constraints led to insufficient information obtained to produce a diagnosis. This limitation is overcome by developing an expert system using fuzzy logic to represent the vagueness of symptoms experienced by patients and the certainty factor represents a relationship between the symptoms and disease. Fuzzy logic method begins with the acquisition of knowledge to produce the facts and rules, implication process, composition and defuzzification. The result of defuzzification used in the calculation of sequential and combined certainty factor which represent the belief percentage of diseases diagnosis that suffered by the patient. The results of the expert diagnosis with expert system for the given cases indicates the system, has the similarity diagnosis with the expert at 93.99%

Medical Expert Systems Survey

There is an increase interest in the area of Artificial Intelligence in general and expert systems in particular. Expert systems are rapidly growing technology. Expert system is a branch of Artificial Intelligence which is having a great impact on many fields of human life. Expert systems use human expert knowledge to solve complex problems in many fields such as Health, science, engineering, business, and weather forecasting. Organizations employing the technology of expert system have seen an increase in the efficiency and the quality. An expert system is computer program that emulates the behavior of a human expert. The expert system represents knowledge solicited from human expert as data or production rules within a computer program. These rules and data can be used to solve complex problems. In this paper, we give an overview of this technology and will discuss a survey on many papers done in health using expert system