Determining Recommendations for Preventing Elderly People from Cardiovascular Disease Complication Using Objective Distance

Chaising, Supansa; Temdee, Punnarumol

doi:10.1109/gws.2018.8686527

Cited by 6 publications

(9 citation statements)

References 11 publications

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“…2) Determining the entropy of each attribute: To obtain the entropy of each attribute, the objective distance [17]- [18] is initially modified to compute the acceptable distance (…”

Section: ) Proposed Igd Methodsmentioning

confidence: 99%

“…The selected individual attributes represent the relevant attributes affecting the development of hypertension levels for each elderly person. The IGD method is modified from information gain and combined with the concept of objective distance [17]- [18]. Information gain is known as one of the most utilized attribute selection methods to choose irrelevant attributes [30]- [31].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The above risk factors affecting hypertension development are based on previous studies [17]- [18]. However, physical activity was not considered in this study because the data gathered by hospitals related to a patient's physical activity is inadequate.…”

Section: ) Clinical Datamentioning

confidence: 99%

“…The expected level and acceptable level of TC, LDL-C, and HDL-C demonstrated in Table 2 were obtained from the previous work [17]- [18]. The levels of BMI are based on the literature and the experts' suggestion.…”

Section: ) Sample Calculationmentioning

confidence: 99%

“…More specifically, this study proposes a novel attribute selection method called information gain based distance (IGD) for individual attribute selection to obtain better classification performance. The IGD method is a modification of information gain to the original objective distance [17]- [18]. The objective distance is used for determining a distance between the current health status and the expected health status to achieve the target goals for individuals.…”

Section: Introductionmentioning

confidence: 99%

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Individual Attribute Selection Using Information Gain Based Distance for Group Classification of Elderly People With Hypertension

2021

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Attribute selection is the process of selecting relevant attributes being used in model construction to enhance model accuracy. For general medical oriented classification applications, classical attribute selection methods principally select common attributes in the dataset for all individuals. The idea of using individual attributes is proposed in this study to represent the difference among individuals for self-diagnosis. Consequently, this study proposes a new attribute selection method, called information gain based distance (IGD), for individual attribute selection, which represents an individual's health condition differently and can be used for effective classification. The proposed method combines the concept of information gain and objective distance to select individual attributes affecting classification. The IGD method is expected to provide higher classification performance than classical attribute selection methods. To assess the performance of the IGD method, classification accuracy between data with classical attribute selections and with the IGD method is compared. The case study is conducted with 971 secondary data used for group classification of elderly people with hypertension. The classification result of different classifiers was compared, including K-nearest neighbors, neural network, and naive Bayes. The comparison revealed that the classification of data with the IGD attribute selection method provided an average classification accuracy of 98.73%. In comparison, those classifications of data with classical attribute selection methods provided 62.99%, 62.99%, 62.62%, and 62.85% for information gain, Gini index, chisquared, and decision tree, respectively. The results showed that data classification with the IGD method provided higher performance than those with the classical attribute selection methods.

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“…2) Determining the entropy of each attribute: To obtain the entropy of each attribute, the objective distance [17]- [18] is initially modified to compute the acceptable distance (…”

Section: ) Proposed Igd Methodsmentioning

confidence: 99%