An expert system for the differential diagnosis of erythemato-squamous diseases

Güvenir, H. Altay; Emeksiz, Narin

doi:10.1016/s0957-4174(99)00049-4

Cited by 75 publications

(41 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The system consists of an expert shell, which performs the inference, and a rule-base, which contains the knowledge with which the system operates. Güvenir and Emeksiz have developed an expert system for differential diagnosis of erythemato-squamous diseases incorporating decision by classification algorithm [12]. Samy and Alaa [13], had developed expert system that helps to help dermatologists in diagnosing some of the skin diseases.…”

Section: Related Workmentioning

confidence: 99%

The Development of Online Children Skin Diseases Diagnosis System

Yusof¹,

Aziz²,

Fei³

2013

IJIET

View full text Add to dashboard Cite

Abstract-Human habits tend to assume that certain skin diseases are not severe problem. If their children skin had been infected by certain skin diseases, most of the parents or guardian will try to treat this infected skin on their own. However, sometimes this treatment was not suitable with that particular skin problem and can make it become worse. This paper proposes a development of an Online Children Skin Diseases Diagnosis System. This system enables user to recognize skin diseases faced by children through online and provide user advises or treatments in shorter time period. To do so, user will answer questions based on their skin condition or symptoms. It will then helps in detecting the skin diseases and provide treatment suggestion. A rule based and forward chaining inference engine has been used for the development of the system. With this system, it help and allows user recognize children skin diseases via online system and provide useful suggestion to user.Index Terms-Children skin diseases, rule based, expert system. I. INTRODUCTIONSkin diseases are diseases that may originate inside the body and manifest on the skin or start from the skin and manifest on the skin [1]. Some common skin diseases are eczema and rash can infected children and adults. For instances, Atopic Eczema can affects 2 in every 10 people in Malaysia and about 10% -14% of children below the age of 14 [2]. Basically, if children skin had been infected by certain skin diseases, most of the parents or guardian will try to treat this infected skin on their own. However, apparently this treatment was not suitable with that particular skin problem and can make it become worse. These cause disruption for children during their daily routine especially for those children that started attending school.Human habit tend to assume that certain skin diseases are not severe problem, and they tend to not seeing the medical expert in helping diagnose skin diseases. One more problem with skin disease is the difficulty in recognizing the main cause. This occurs because the symptoms of different diseases are quite similar with each other. Therefore, the Online Children Skin Diseases Diagnosis System is developed to help parents to recognize the particular children skin diseases which infected their children in short time period. In the development of this system, the concept of expert system is used. This system will provide question list for parents to choose the symptom as answer and these answers will help in generating diagnosis result.In this paper, we place our focus on common children skin diseases. The main objective of this paper is to develop expert system for diagnosing the children skin diseases in near future. There are six types of common diseases have been identified for this research; viral rash, eczema, impetigo, urticaria, chicken pox and fifth disease. As mention before, the mistake in diagnosing this skin disease can lead to more serious infection. Hence, this research is important for user with the purpose to assist them in det...

show abstract

Section: Related Workmentioning

confidence: 99%

The Development of Online Children Skin Diseases Diagnosis System

Yusof¹,

Aziz²,

Fei³

2013

IJIET

View full text Add to dashboard Cite

show abstract

“…The VFI5 algorithm achieved 96.2% accuracy on the Dermatology dataset with 22 histopathological features. Guvenir and Emeksiz [4] presented an expert system for differential diagnosis of erythematosquamous diseases incorporating decision made by three classification algorithms: nearest neighbor classifier, naïve Bayesian classifier and voting feature intervals-5. They obtained 99.2% classification accuracy on the differential diagnosis of erythemato-squamous diseases.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Feature Selection Method based on IGSBFS and Naive Bayes for the Diagnosis of Erythemato - Squamous Diseases

Aruna¹,

Nandakishore²,

Rajagopalan³

2012

IJCA

View full text Add to dashboard Cite

In this paper a diagnostic model based on Naive Bayes developed to diagnose erytemato squamous diseases. The hybrid feature selection method, named IGSBFS (Information Gain and Sequential Backward Floating Search), combines the advantages of filters and wrappers to select the optimal feature subset from the original feature set. In IGSBFS, Information Gain acts as filters to remove redundant features and SBFS with Naïve Bayes acts as the wrappers to select the ideal feature subset from the remaining features We conducted experiments in WEKA with 10 fold cross validation. The algorithm selected an optimum feature subset of 10 features with 98.9% accuracy.

show abstract

“…They project the training instances on each feature separately, and then generalize on these projections to form intervals [6,[13][14][15][16][17][18]45]. In those studies, segments (intervals) are taken to be the basic unit of concept representation; and the classification knowledge is represented in the form of segments formed on each feature.…”

Section: Feature Projections Conceptmentioning

confidence: 99%

“…Ko and Seo applied feature projections to the text categorization problem [27]. In the work presented in [17,18,45], a segment represents examples from a single class, whereas the authors in [6,14,15] allow a segment to represent examples from a set of classes instead of a single class. We prefer to define the segment term to be a unit of concept description that represents examples from a set of classes.…”

Section: Feature Projections Conceptmentioning

confidence: 99%

Modeling interestingness of streaming association rules as a benefit-maximizing classification problem

Aydın

Güvenir

2009

Knowledge-Based Systems

View full text Add to dashboard Cite

a b s t r a c tIn a typical application of association rule learning from market basket data, a set of transactions for a fixed period of time is used as input to rule learning algorithms. For example, the well-known Apriori algorithm can be applied to learn a set of association rules from such a transaction set. However, learning association rules from a set of transactions is not a one time only process. For example, a market manager may perform the association rule learning process once every month over the set of transactions collected through the last month. For this reason, we will consider the problem where transaction sets are input to the system as a stream of packages. The sets of transactions may come in varying sizes and in varying periods. Once a set of transactions arrive, the association rule learning algorithm is executed on the last set of transactions, resulting in new association rules. Therefore, the set of association rules learned will accumulate and increase in number over time, making the mining of interesting ones out of this enlarging set of association rules impractical for human experts. We refer to this sequence of rules as ''association rule set stream" or ''streaming association rules" and the main motivation behind this research is to develop a technique to overcome the interesting rule selection problem. A successful association rule mining system should select and present only the interesting rules to the domain experts. However, definition of interestingness of association rules on a given domain usually differs from one expert to another and also over time for a given expert. This paper proposes a post-processing method to learn a subjective model for the interestingness concept description of the streaming association rules. The uniqueness of the proposed method is its ability to formulate the interestingness issue of association rules as a benefit-maximizing classification problem and obtain a different interestingness model for each user. In this new classification scheme, the determining features are the selective objective interestingness factors related to the interestingness of the association rules, and the target feature is the interestingness label of those rules. The proposed method works incrementally and employs user interactivity at a certain level. It is evaluated on a real market dataset. The results show that the model can successfully select the interesting ones.

show abstract

An expert system for the differential diagnosis of erythemato-squamous diseases

Cited by 75 publications

References 6 publications

The Development of Online Children Skin Diseases Diagnosis System

The Development of Online Children Skin Diseases Diagnosis System

A Hybrid Feature Selection Method based on IGSBFS and Naive Bayes for the Diagnosis of Erythemato - Squamous Diseases

Modeling interestingness of streaming association rules as a benefit-maximizing classification problem

Contact Info

Product

Resources

About