Weighted objective distance for the classification of elderly people with hypertension

Chaising, Supansa; Temdee, Punnarumol; Prasad, Ramjee

doi:10.1016/j.knosys.2020.106441

Cited by 6 publications

(8 citation statements)

References 23 publications

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“…Later, WOD [10], which is the modification of the original OD, was proposed to improve the group classification performance of older people with hypertension by using prioritized individual factors instead of considering all of them. Following the principle of distance measurement, WOD has a scalar factor or weight for representing the priority.…”

Section: B Prediction Methodsmentioning

confidence: 99%

“…For AWOD, the information gain is applied to determine both significant and insignificant factors for individuals. The former is defined as the factors the individual cannot control properly so that they have a noticeable effect on their health condition, while the latter are those controlled by the individual [10]. The limitation of WOD is that fewer significant individual factors decrease classification performance.…”

Section: B Prediction Methodsmentioning

confidence: 99%

“…The proposed method, the Average Weighted Objective Distance (AWOD), is a modification of Weighted Objective Distance (WOD) [10]. Both methods are designed particularly for constructing prediction models from relatively small datasets because of infrequent and rare clinical data.…”

Section: Introductionmentioning

confidence: 99%

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Average Weighted Objective Distance-Based Method for Type 2 Diabetes Prediction

2021

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Early detection of Type 2 diabetes is necessary for its prevention. The prediction models for detection systems normally employ common factors that may not properly fit all persons having different health conditions. Therefore, this study proposes a method for type 2 diabetes prediction with factors representing personal health conditions. More specifically, this study proposes a novel prediction method named Average Weighted Objective Distance (AWOD) based on the assumption that the individual has diverse health conditions resulting from different individual factors, a requirement for an effective prediction model. AWOD is a modification of Weighted Objective Distance (WOD) by applying information gain to reveal significant and insignificant individual factors having different priorities, which are represented by different weights. For AWOD, the data set is divided into a training set used to determine all relevant thresholds and constant values required for AWOD calculation and the testing set. In particular, AWOD is designed for binary classification problems with a relatively small dataset. To validate the proposed method, two datasets from open sources, Pima Indians Diabetes (Dataset 1) and Mendeley Data for Diabetes (Dataset 2) each containing 392 records, were studied. The prediction performance for both datasets is compared with the machine learning-based prediction methods, including K-Nearest Neighbors, Support Vector Machines, Random Forest, and Deep Learning. The comparison results showed that the proposed method provided 93.22% and 98.95% accuracy for Dataset 1 and Dataset 2, respectively, which are higher than those provided by other machine learning-based methods.

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Section: B Prediction Methodsmentioning

confidence: 99%

Section: B Prediction Methodsmentioning

confidence: 99%

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Average Weighted Objective Distance-Based Method for Type 2 Diabetes Prediction

2021

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“…The individual relevant attribute represents the actual effect of attribute toward classification for each individual. The concept of the IGD method is based on the principle of a general diagnosis normally performed by healthcare professionals that focuses on a few significant individual factors of patients affecting disease diagnosis [41]. In addition, the health data obtained from the patients is varied because each person has different health conditions, resulting in different disease diagnoses.…”

Section: ) a Principle Of The Igd Methodsmentioning

confidence: 99%

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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“…Diabetes is detected with 97.5% using extreme learning machine. Recently, a modified version of the Weighted Objective Distance-based (WOD) diabetes prediction engine [17] was proposed that takes into account the individuals' personal health status [18]. The modified WOD makes use of information gained to distinguish positive from negative participants.…”

Section: Related Workmentioning

confidence: 99%

An Ensemble of Light Gradient Boosting Machine and Adaptive Boosting for Prediction of Type-2 Diabetes

Sai

Chettri²,

Panigrahi³

et al. 2023

Int J Comput Intell Syst

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Machine learning helps construct predictive models in clinical data analysis, predicting stock prices, picture recognition, financial modelling, disease prediction, and diagnostics. This paper proposes machine learning ensemble algorithms to forecast diabetes. The ensemble combines k-NN, Naive Bayes (Gaussian), Random Forest (RF), Adaboost, and a recently designed Light Gradient Boosting Machine. The proposed ensembles inherit detection ability of LightGBM to boost accuracy. Under fivefold cross-validation, the proposed ensemble models perform better than other recent models. The k-NN, Adaboost, and LightGBM jointly achieve 90.76% detection accuracy. The receiver operating curve analysis shows that $$k$$ k -NN, RF, and LightGBM successfully solve class imbalance issue of the underlying dataset.

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Weighted objective distance for the classification of elderly people with hypertension

Cited by 6 publications

References 23 publications

Average Weighted Objective Distance-Based Method for Type 2 Diabetes Prediction

Average Weighted Objective Distance-Based Method for Type 2 Diabetes Prediction

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

An Ensemble of Light Gradient Boosting Machine and Adaptive Boosting for Prediction of Type-2 Diabetes

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