Semantic Web for Effective Healthcare Systems. Группа авторов

modeling is done again for the Ontology update. The process of querying is repeated as one of the other three types described earlier.

      1.5.4 Metrics Analysis

Accuracy is measured as a performance measure for the number of terms correctly identified under the topic (or features). However, the model is further evaluated by the metrics such as precision, recall, and f-measure. Precision defines how precise the model is, and recall defines how complete the model is. It is represented as a confusion matrix, which contains information about actual and predicted results, as shown in Table 1.1.

Table 1.1 Confusion matrix.

	Predicted positive	Predicted negative
Actual positive	TP	FIN
Actual negative	FP	TN

      where

      TP: the number of correct classifications of the positive examples (true positive)

      FN: the number of incorrect classifications of positive examples (false negative)

      FP: the number of incorrect classifications of negative examples (false positive)

      TN: the number of correct classifications of negative examples (true negative)

Precision is defined as the percentage of correctly identified documents among the documents returned; whereas recall is defined as the percentage of relevant results among the correctly identified documents. Practically, high recall is achieved at the expense of precision and vice versa [61]. However, the metric f-measure is suitable when single metric is needed to compare different models. It is defined as the harmonic mean of precision and recall. Based on the confusion matrix, the precision, the recall, and the f-measure of the positive class are defined as

(1.3)

(1.4)

(1.5)

(1.6)

Ontology-based Semantic Indexing (OnSI) model is evaluated by the metrics such as precision, recall, and accuracy, as shown in Equations 1.3, 1.4, 1.5, and 1.6.

1.6 Dataset Description

Document collection like Healthcare service reviews (Dataset DS1) were collected from different social media web sites for 10 different hospitals, as detailed in Tables 1.2 and 1.3.

Table 1.2 Social media reviews for healthcare service (DS1).

	Number of reviews
Data source	Positive	Negative
Twitter	1200	525
Mouthshut.com	425	110
BestHospitalAdvisor.com	200	85
Google Reviews	580	320
Total Reviews	2405	1040

Table 1.3 Number of reviews of features and hospitals (DS1).

Features	Reviews
Cost	663
Medicare	748
Nursing	776
Infrastructure	554
Time	704

Number of reviews
H1	596	H6	308
H2	411	H7	313
H3	399	H8	297
H4	227	H9	281
H5	252	H10	361

      Table 1.3 shows that 663 reviews were collected for the feature “cost,” and 596 reviews were collected for the hospital H1. The list of features is identified from the previous work, related articles, and social media websites. For example, the document [62] clearly identified the necessary criteria for healthcare services. The expectations of patients such as cost, hospital characteristics, infrastructure facility, recommendations by other users, treatment and nursing care, and medical