Risk of Anemia Among Elementary School Students in East Kalimantan Province, Indonesia; A Case Control Protocol Study

Nugroho, Purwo Setyo; Sunarti, Sri; Amalia, Nida; Tianingrum, Niken Agus; Kurniasari, Lia; Winarti, Yuliani; Febriyanto, Kresna; Oktaviani, Lisa Wahidatul; Susanti, Erni; Rahman, Ferry Fadzlul; Ghozali, Ghozali; Rachman, Ainur

doi:10.33486/jrphs.v1i2.23

Cited by 6 publications

(6 citation statements)

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“…Masalah diabetes melitus di Indonesia sudah terjadi sejak tahun 1980 sampai sekarang. Jumlah penderita diabetes mellitus di Indonesia diperkirakan sekitar 10 juta orang dengan angka prevalensi 6,2% dan penyakit ini merupakan salah satu penyebab kematian di Indonesia [12]. Jumlah penderita diabetes mellitus di kalangan orang dewasa diperkirakan mencapai 463 juta (9,3% dari total populasi) di seluruh dunia pada 2019 [13].…”

Section: Optimasi Algoritma K-nearest Neighbors Dengan Teknik Cross V...unclassified

Optimization of K-Nearest Neighbors Algorithm with Cross Validation Techniques for Diabetes Prediction with Streamlit

Prasetyo¹,

Laksana²

2022

JAIC

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The problem that occurs in the application of K-Nearest Neighbors as a classification algorithm is the frequent occurrence of overfitting in data processing. This can be overcome by using cross-validation techniques in evaluating the algorithm model and minimizing overfitting. Then the performance of diabetes prediction accuracy is unknown using the K-Nearest Neighbors algorithm with cross-validation technique. The data used comes from the National Institute of Digestive and Kidney Diabetes in 2021. The case study in this study is to find out the initial screening for diabetes is supported by the results of algorithm accuracy and real time application of streamlit-based users. The purpose of this study was to optimize the accuracy results with a cross validation technique supported by the k-nearest neighbors algorithm in the study of diabetes data. The method used is the k-nearest neighbors algorithm which is supported by cross validation technique for optimal accuracy results. Then the application of a streamlit-based interactive web application for testing the accuracy results used by the user to see the probability that the user has diabetes. The results showed that the optimization of the Cross Validation technique supported by the KNearest Neighbors algorithm model worked well. The results of the confusion matrix using the cross validation technique are more accurate in terms of the advantages of using the cross-validation technique itself. So that the classification report which has a value of 95% is more accurate than the accuracy which is worth 92% because of the use of cross-validation techniques that can minimize overfitting in addition to considerations of the accuracy value and the implementation of streamlit-based interactive web applications for user testing is going well.

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Section: Optimasi Algoritma K-nearest Neighbors Dengan Teknik Cross V...unclassified

Optimization of K-Nearest Neighbors Algorithm with Cross Validation Techniques for Diabetes Prediction with Streamlit

Prasetyo¹,

Laksana²

2022

JAIC

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“…Nurses are the most numerous health workers in the Posbindu program and are always involved in all activities (Health Social Security Administrator, 2014 ). Several studies have been conducted on diabetes risk factors in Indonesia, including identifying developmental factors during childhood with the incidence of diabetes when they are adults between the ages of 20-40 years (Tanoey & Becher, 2021 ), examining fasting blood glucose levels among older adults (Oktaviyani et al, 2022 ), and identifying the most dominant risk factors for diabetes (Nugroho et al, 2020 ). In addition, these studies used secondary data from the Indonesian Family Life Survey and Basic Health Research on people diagnosed with diabetes.…”

Section: Introductionmentioning

confidence: 99%

Identifying diabetes risks among Indonesians: A cross-sectional study in a community setting

Tarigan,

Setiawan,

Tarigan

et al. 2024

Belitung Nurs J

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Background: There is an upward surge in diabetes patients worldwide, including in Indonesia, annually. Diabetes can lead to new diseases that burden patients’ lives further. Nurses can reduce this problem by identifying people at risk of developing diabetes and educating them on how to prevent diabetes. Objective: The study aimed to determine the risk of diabetes in the Indonesian population. Methods: The descriptive research involved a sample of 1216 Indonesians living in North Sumatra Province. Participants were nondiabetic individuals selected using the convenience method from May to October 2020. This study utilized the Indonesian version of the Finnish Diabetes Risk Score (FINDRISC) tool and employed various statistical analyses, including frequencies, percentages, chi-square test, and Fisher’s exact test. Results: Of the total samples, 372 were males (30.6%), and 844 were females (69.4%). The risk of developing diabetes was classified as low (57.1%), slightly elevated (36.4%), moderate (5.3%), high (1.0%), and very high (0.2%). Only one of the eight risk factors that differed significantly between men and women was a history of elevated blood glucose levels, with a p-value of 0.02. Conclusion: The study identified a portrait of the number and percentage of diabetes risk factors in a community setting in Indonesia. Nurses must provide education on diabetes prevention to not only members of the local community at the research site but also the general public, nationally and globally.

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“…Indonesia has the seventh-highest prevalence of diabetes mellitus in the world, after China, India, the USA, Brazil, Russia, and Mexico, and it is predicted that in 2045 it will hold the eighth-highest position with a prevalence of 16,6 million people have diabetes ( 1 , 3 ). According to data from Basic Health Research, 69.6% of people with diabetes in Indonesia were undiagnosed, while the prevalence of the disease was 6.9% in 2013 and 8.5% in 2018 ( 4 ). According to estimates, 14.1 million people in Indonesia will have diabetes by 2030.…”

Section: Introductionmentioning

confidence: 99%

Identifying Adult Population at Risk for Undiagnosed Diabetes Mellitus in Medan City, Indonesia Targeted on Diabetes Prevention

Siregar,

Asfriyati,

Makmur

et al. 2023

Med Arch

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Background: Diabetes mellitus is a health problem in Indonesia, where its prevalence rises annually. The condition may negatively impact one's quality of life and lead to significant complications—over 50% of patients with type 2 diabetes mellitus, the most common diabetes type worldwide. To implement diabetic prevention interventions and achieve effective diabetes mellitus control, screening for undiagnosed diabetes mellitus in high-risk populations is essential. Objective: This study aimed to identify people at risk for undiagnosed diabetes mellitus using the Finnish Diabetes Risk Score (FINDRISC) and oral glucose tolerance test (OGTT). Methods: This cross-sectional study was carried out, which involved 300 people in Medan City between the ages of 30-75. The study was conducted between July 14 and October 20, 2020. The Finnish Diabetes Risk Score and anthropometric measurements (weight, height, and waist circumference) were used to evaluate each respondent. Low, medium, and high-risk outcome categories were created. All responders underwent oral glucose tolerance tests, with results classified as normal, prediabetes, or diabetes. The Chi-square test was used to examine the data and identify potential risk variables for diabetes mellitus. Results: Of 300 individuals, 91.7% were female, and 8.3% were male. 33.7% of participants had a low risk of acquiring diabetes, 55.0% had an intermediate risk, and 11.3% had a high risk. 70% of participants who took an oral glucose tolerance test had blood glucose levels that were normal (less than 100 mg/dl), 18.7% had blood glucose levels suggestive of prediabetes (between 100 and 125 mg/dl), and 11.3% had blood glucose levels suggestive of diabetes (126 mg/dl or higher). Ages 54 to 64 years and older, high body mass index and central obesity, a lack of vegetables and fruits in the daily diet, inactivity, uncontrolled hypertension, a history of hyperglycemia, and a family history of diabetes were all linked to a significant increase in the risk of developing diabetes. Most respondents at low risk of developing type 2 diabetes had normal blood sugar levels, while those at high risk of developing type 2 diabetes had high blood glucose levels (p = 0.005). Conclusion: Oral glucose tolerance testing and the FINDRISC questionnaire may be used to identify individuals who are at high risk for developing diabetes and to encourage them to adopt healthy behaviours.

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Risk of Anemia Among Elementary School Students in East Kalimantan Province, Indonesia; A Case Control Protocol Study

Cited by 6 publications

References 0 publications

Optimization of K-Nearest Neighbors Algorithm with Cross Validation Techniques for Diabetes Prediction with Streamlit

Optimization of K-Nearest Neighbors Algorithm with Cross Validation Techniques for Diabetes Prediction with Streamlit

Identifying diabetes risks among Indonesians: A cross-sectional study in a community setting

Identifying Adult Population at Risk for Undiagnosed Diabetes Mellitus in Medan City, Indonesia Targeted on Diabetes Prevention

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