Cardiovascular disease prediction model for Indonesian adult population with prediabetes and diabetes mellitus: The Bogor Cohort study of Noncommunicable Diseases Risk Factors

Sibarani, Marcel H.R.; Wijaya, Ika Prasetya; Rizka, Aulia; Soewondo, Pradana; Riyadina, Woro; Rahajeng, Ekowati; Sudikno, Sudikno; Harbuwono, Dante Saksono; Tahapary, Dicky L.

doi:10.1016/j.dsx.2021.102330

Cited by 4 publications

(3 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This underscores the escalating public health challenge posed by CVD, both on a global scale and within Indonesia. It emphasizes CVD's significance as a focal point for healthcare system improvements and acknowledges its substantial impact on mortality rates and socioeconomic burdens in the country (Bahruddin et al, 2024;Hikmayani & Rachmawan, 2024;Lawton et al, 2024;Luh Putu Maitra Agastya et al, 2024;Mboi et al, 2022;Organization, 2022;Roth et al, 2020;Sibarani et al, 2022;Utama et al, 2024).…”

Section: Introductionmentioning

confidence: 99%

Surrogate Biomarker to Identify Obesity and Predict Cardiovascular Disease Risk: A Systematic Review

Azzumar,

Helda,

Salamah

et al. 2024

JHS

View full text Add to dashboard Cite

The study investigates the relationship between obesity and cardiovascular disease (CVD) risk, comparing the use of body mass index (BMI) versus other biomarkers in predicting CVD risk among obese individuals. It conducts a systematic literature review following the PRISMA guidelines, searching databases for relevant articles published from 2017-2022. The review analyzes 12 eligible articles and finds that factors beyond just BMI, such as genetics, physical activity, metabolic disorders, previous heart disease history, nutrition, fat distribution, and changes in BMI, can significantly impact the prognosis of heart disease in obese individuals. Importantly, the study shows that measures of fat distribution, like waist-to-height ratio, waist circumference, log-transformed body shape index (LBSIZ), and the ratio of visceral adipose tissue (VAT) to subcutaneous adipose tissue (SAT), are superior to BMI in predicting CVD risk among those with obesity. The key takeaway is that while obesity is strongly linked to CVD risk, BMI alone often fails to predict that risk accurately. Fat distribution measures may be a more effective tool for identifying obesity status and predicting associated CVD risk compared to relying solely on BMI.

show abstract

Section: Introductionmentioning

confidence: 99%

Surrogate Biomarker to Identify Obesity and Predict Cardiovascular Disease Risk: A Systematic Review

Azzumar,

Helda,

Salamah

et al. 2024

JHS

View full text Add to dashboard Cite

show abstract

“…They have outlined with regards to how the datasets accessible for coronary illness are by and large crude in nature which is profoundly excessive and conflicting. There is a need for the pre-handling of these informational collections; in this stage, the layered informational collection is decreased to the low dataset (Sibarani et al 2021). An optimization of features has been done to accomplish higher classification efficiency in the Decision Tree.…”

Section: Introductionmentioning

confidence: 99%

Improved Accuracy in Heart Disease Prediction using Novel Random Forest Algorithm in Comparison with Support Vector Machine Algorithm

Poojitha¹,

Mahaveerakannan²

2023

View full text Add to dashboard Cite

Aim: The primary aim of this paper is to improve the exactness of the classification of coronary illness predictions using the help of machine learning algorithms. We will be comparing the novel Random Forest with the Support Vector Machine to find out which of these can give us the best accuracy. Material and Methods: The study used 184 samples with Novel Random Forest and Support vector machine is executed with varying training and testing splits for anticipating the accuracy for coronary illness prediction with the 80% of G-power value and the Coronary disease data were gathered from several web sources and included current research findings, a threshold of 0.05%, a confidence range of 95%, a mean, and a standard deviation. The accuracy rate of the classifiers is used to evaluate their performance using the Coronary disease dataset. The difference between the two groups is statistically negligible. (p = 0.225; p > 0.05). Results and Discussion: The accuracy of predicting heart disease in Novel Random Forest is 90.16% and support vector machine is 81.97%. Conclusion: Expectation of heart infection using the Novel Random Forest (RF) algorithm appears to be significantly better than the Support Vector Machine (SVM) with improved accuracy.

show abstract

“…From the point of view of persons with disabilities themselves, as vulnerable people, the main reason for granting concessions is that there are additional costs that must be incurred by people with disabilities (the "Disability Extra Cost"), whereas these additional costs are not experienced by people without disabilities. (Sibarani et al 2022) In preparing government regulations, the community, organizations of persons with disabilities, and the seriousness and concern of all parties must be involved, primarily the provincial government, the central government, the city government, and all levels of society. Based on juridical references in Articles 114-116 of Law Number 8 of 2016, concerning Persons with Disabilities, and Article 12 of Law No.…”

Section: Introductionmentioning

confidence: 99%

Disabilities concessions in Indonesia: fundamental problems and solutions

Permatasari

Hermawan²,

Jaelani³

2022

LJIH

View full text Add to dashboard Cite

There are many people with disabilities in Indonesia. However, the government has not fulfilled the rights of persons with disabilities according to the provisions in the laws and regulations on persons with disabilities. This study aims to analyze concessions for persons with disabilities in Indonesia: fundamental problems and offered solutions. This research is a normative study using the Literature Review study approach, namely by examining and analyzing data. The results of the research show that the government has not guaranteed the rights of persons with disabilities in Indonesia, so it is essential to issue government regulations that regulate the type and amount of concessions as a form of creating legal certainty for persons with disabilities. This concession is essential to implement in Indonesia because it can reduce the barriers for persons with disabilities to access essential services to increase their income and economic participation.

show abstract

Cardiovascular disease prediction model for Indonesian adult population with prediabetes and diabetes mellitus: The Bogor Cohort study of Noncommunicable Diseases Risk Factors

Cited by 4 publications

References 26 publications

Surrogate Biomarker to Identify Obesity and Predict Cardiovascular Disease Risk: A Systematic Review

Surrogate Biomarker to Identify Obesity and Predict Cardiovascular Disease Risk: A Systematic Review

Improved Accuracy in Heart Disease Prediction using Novel Random Forest Algorithm in Comparison with Support Vector Machine Algorithm

Disabilities concessions in Indonesia: fundamental problems and solutions

Contact Info

Product

Resources

About