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Copyright of Plexus Medical Journal (PMJ) is the property of Sebelas Maret University, Faculty of Medicine / Universitas Sebelas Maret, Fakultas Kedokteran and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Plexus Medical Journal (PMJ) is the property of Sebelas Maret University, Faculty of Medicine / Universitas Sebelas Maret, Fakultas Kedokteran and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Introduction: The prevalence of metabolic syndrome in Indonesia is still high, reaching 23%. This syndrome is still the biggest risk factor for blood vessel disease. Prevention of this syndrome is very important to reduce the incidence. Screening for risk factors for metabolic syndrome in the elderly who are a vulnerable group is very necessary. Methods: This service was carried out for the elderly on Sunday, May 7 at the Karang Village Hall, District. Karangpandan, Karanganyar Regency, Central Java. Blood pressure, blood sugar and anthropometric examinations were carried out in the form of height and weight to determine body mass index. The service provides pharmacological therapeutic interventions and education to elderly people who are identified as having risk factors for metabolic syndrome. Results and discussions: Screening is preceded by carrying out exercises with the elderly. Of the 48 elderly posyandu participants, the incidence of hypertension was found to be 38 elderly participants (79,17%), which was divided into grade 1 and 2 hypertension. The results of blood sugar measurements showed that 20.83% of elderly participants had blood sugar levels =200mg which could be diagnosed as diabetes mellitus sufferers. The body mass index of the elderly was found to be 72.92% of the elderly who were overweight (lightly obese and heavily obese). Conclusion: Screening for metabolic syndrome in the elderly found that >70% of the elderly suffered from hypertension and were obese and 20.83% had diabetes mellitus. Service providers provide education and provide necessary treatment according to the signs and symptoms experienced by elderly people who experience metabolic syndrome. [ABSTRACT FROM AUTHOR]

Copyright of Plexus Medical Journal (PMJ) is the property of Sebelas Maret University, Faculty of Medicine / Universitas Sebelas Maret, Fakultas Kedokteran and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Plexus Medical Journal (PMJ) is the property of Sebelas Maret University, Faculty of Medicine / Universitas Sebelas Maret, Fakultas Kedokteran and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The selection of the correct examination variables for diagnosing heart disease provides many benefits, including faster diagnosis and lower cost of examination. The selection of inspection variables can be performed by referring to the data of previous examination results so that future investigations can be carried out by referring to these selected variables. This paper proposes a model for selecting examination variables using an Artificial Bee Swarm Optimization method by considering the variables of accuracy and cost of inspection. The proposed feature selection model was evaluated using the performance parameters of accuracy, area under curve (AUC), number of variables, and inspection cost. The test results show that the proposed model can produce 24 examination variables and provide 95.16% accuracy and 97.61% AUC. These results indicate a significant decrease in the number of inspection variables and inspection costs while maintaining performance in the excellent category. [ABSTRACT FROM AUTHOR]

Coronary heart disease (CHD) is a comorbidity of COVID-19; therefore, routine early diagnosis is crucial. A large number of examination attributes in the context of diagnosing CHD is a distinct obstacle during the pandemic when the number of health service users is significant. The development of a precise machine learning model for diagnosis with a minimum number of examination attributes can allow examinations and healthcare actions to be undertaken quickly. This study proposes a CHD diagnosis model based on feature selection, data balancing, and ensemble-based classification methods. In the feature selection stage, a hybrid SVM-GA combined with fast correlation-based filter (FCBF) is used. The proposed system achieved an accuracy of 94.60% and area under the curve (AUC) of 97.5% when tested on the z-Alizadeh Sani dataset and used only 8 of 54 inspection attributes. In terms of performance, the proposed model can be placed in the very good category. [ABSTRACT FROM AUTHOR]

Coronary heart disease is a non-communicable disease with high mortality. A good action to anticipate this is to do prevention, namely by carrying out a healthy lifestyle and routine early examinations. Early detection of coronary heart disease requires a number of examinations, such as demographics, ECG, laboratory, symptoms, and even angiography. The number of inspection parameters in the context of early detection will have an impact on the time and costs that must be incurred. Selection of the right and important inspection parameters will save time and costs. This study proposes an intelligence system model for the detection of coronary heart disease by using a minimal examination attribute, with performance in the good category. This research method is divided into a number of stages, namely data normalization, feature selection, classification, and performance analysis. Feature selection uses a Two-tier feature selection framework consisting of correlationbased filters and wrappers. The system model is tested using a number of datasets, and classification algorithms. The test results show that the proposed two-tier feature selection framework is able to reduce the highest attribute of 73.51% in the z-Alizadeh Sani dataset. The performance of the system using the bagging-PART algorithm is able to provide the best performance with parameters area under the curve (AUC) 95.4%, sensitivity 95.9% while accuracy is 94.1%. Referring to the AUC value, the proposed system model is included in the good category. [ABSTRACT FROM AUTHOR]