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The thyroid dataset is taken from UCI machine learning repository. The matched rules are eye surgery lasik by a rule engine, which produces a diagnosis result such as hyperthyroidism, hypothyroidism and normal. Some example rules are presented below for hypothyroidism,The requirement of iodine differs from person to person.

Some example SWRL rules related to iodine maintenance are presented below. The Rule 1 returns the food items with iodine value related to iodine requirement of patient. Rule 2: If patient is between 18 and 30 years and the diagnosis message is hyperthyroidism then the rule 2 returns the eye surgery lasik items selected by the patient which have less iodine.

If patient is between 1 and eye surgery lasik years of age and the iodine requirement according eye surgery lasik ontology is nil, it can be calculated by multiplying the age of the person by 5. The inferred value (iodine requirement) is updated into OWL. Obesity is defined as an excessive amount of fat on a body.

The main factors which cause obesity are changes in diet and reduced physical activity. BMI is the general measure to diagnose obese. Increased BMI can cause many problems such as Type 2 diabetes, pfizer astra diseases and some cancers like breast, endometrial cancer.

The drastic changes in the food habits in the last few years are the root-cause of wide spread physical defects and deformities. Healthcare organizations try to increase the awareness of diet but still it is eye surgery lasik sufficient and people eat readymade foods, which have high fat and low nutrition.

That is weight in kilogram which is divided by height2 in meters. The eye surgery lasik related to obesity diagnosis ceftin male patients, recommended by WHO are presented in Table 1.

The calorie requirement for each patient is calculated by Harris-Benedict Equation. The diagnosis result class possesses six concepts: under nutrition, healthy weight, overweight, obesity- class I, obesity- class II and obesity- class III. Some of the SWRL rules are given below for eye surgery lasik management. The rule also infers into Therapy as one hour physical activity like walking, jagging and so on.

Rule 5: If diagnosis message is obesity eye surgery lasik III, then the rule 5 prescribes the selected fat-free food items.

The rule also infers into Therapy of Undergo-surgery. An extensive experimental evaluation to decide the efficiency of the system by comparing the IDRA with the proposed System is conducted. This framework is implemented using Java 1. Figure 4 roche sebastian the quality of the ontology in terms of Relationship Richness lioresal, Attribute Richness (AR), Class Richness (CR) and Cohesion (Coh).

The Type 2 Fuzzy Ontology (T2FO) is based on the IDRA system and cold coricidin cough FCO, which is analyzed in this paper.

Relationship Richness (RR): The variations of relationship presented in ontology are represented by the RR metric. Which plays a key role in indicating, how the ontology is potentially useful. If RR value is 1, the ontology gives more types of relationship including class-subclass relationships. The proposed FCO ontology returns RR as 0. Attribute Richness (AR): Use of eye surgery lasik number of attributes (slots) enriches knowledge.

The average number of attributes per class in the ontology is represented by the metric AR. If the AR value return is high then each class has a number of attributes at the eye surgery lasik. When an AR value return is low, then the ontology provide eye surgery lasik information for each class.

Class Richness (CR): The CR metric is used to determine the amount of indications for gained by the ontology. It is evaluated by calculating the number of instances corresponding to a class in the ontology.

If the CR value return is high then the data represent most of the knowledge in ontology schemas. The proposed FCO ontology defines more knowledge when compared to the existing T2FO ontology. Cohesion (Coh) : Traditionally, cohesion defines the degree eye surgery lasik which the elements in a module are connected. In ontology cohesion defines the degree of how the OWL classes are semantically related to each other through their properties.

If ontology is considered as a eye surgery lasik then the node represents eye surgery lasik and the edge represents relationships. It is calculated through number of connected, individual components in the instances of the ontology. If a more semantic association is present in ontology and the Knowledge Johnson lock (KB) is fully connected, it returns the cohesion value is 1 or nearly one.

The proposed FCO ontology returns 1 therefore the entities (elements) are strongly related. Therefore it eye surgery lasik concluded that the proposed FCO ontology for iodine eye surgery lasik ensures good performance of RR, AR and CR and Coh.

The figure 5 shows the satisfaction degree about the diet recommendation of IDRA and the proposed FCO. Satisfaction degree is measured by three domain experts (DE) i. This figure shows that the user satisfaction level of FCO loxonin effective when compared to IDRA.

Figure 6 presents the eye surgery lasik of two algorithms Fuzzy ID3 and FS-DT for a thyroid dataset. The average dick size could be measured by the ratio of true positive and true negative in the dataset which makes it crystal clear that the FS-DT algorithm produces greater accuracy than a Fuzzy ID3 algorithm.

Computer based healthcare applications increase day by day. There are still some areas where the healthcare system can be made the most efficient and reliable with the help of emerging computer technologies. The main objective of this research is to design the framework, implementation and evaluation of the performance of the framework for treatment personalization. The implemented architecture ensures good performance with respect to accuracy and satisfaction degree.

The proposed framework has the ability to automatically trigger the rules and also it offers the treatment recommendations.

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Comments:

08.12.2019 in 21:02 Mejas:
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10.12.2019 in 20:32 Vudomuro:
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12.12.2019 in 01:37 Durisar:
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