Classification of diabetes mellitus disease at Rato Ebuh Hospital-Indonesia using the K-Nearest neighbors method based on missing value DOI Creative Commons
Sigit Susanto Putro,

Moh Abdan Syakura Putra,

Doni Abdul Fatah

et al.

BIO Web of Conferences, Journal Year: 2024, Volume and Issue: 146, P. 01081 - 01081

Published: Jan. 1, 2024

Diabetes mellitus is a chronic disease often caused by high blood glucose levels and insufficient insulin production. This research aims to address the classification problem of diabetes using K-Nearest Neighbor (K-NN) method. The aim this create machine learning model that can detect early. study was conducted at Syarifah Ambami Rato Ebu Hospital in Bangkalan, utilizing data from 120 patients 2019, employing mining techniques classify patients. Additionally, steps involve determining significant variables or features for Cleansing normalization transformation. compares training test results with ratios 90:10, 80:20, 70:30. Experimental show K-NN neighbor value K=11 achieves highest accuracy rate 83% reduced error 16.67%, AUC 0.7407. These indicate 90:10 split ratio yields best performance terms class differentiation mellitus, as well lowest compared other ratios. provides better understanding demonstrates effective addressing problems, focusing on specific influence disease. Therefore, it be concluded suitable algorithm classifying mellitus.

Language: Английский

The positive implication of natural antioxidants on oxidative stress-mediated diabetes mellitus complications DOI Creative Commons

Shouvik Mallik,

Bijoy Paria,

Sayed Mohammad Firdous

et al.

Journal of Genetic Engineering and Biotechnology, Journal Year: 2024, Volume and Issue: 22(4), P. 100424 - 100424

Published: Sept. 10, 2024

The complementary intervention to modulate diabetes mellitus (DM) metabolism has recently brought the global attention, since DM become among burden diseases. Where, several related pathways elevate production of superoxide in consequences. For example, flux glycation-derived end products (AGEs) could lead deactivation insulin signaling pathways. In that context, many vitamins and phytochemicals natural sources have high antioxidant impacts reduce oxidative stress cell damages. These chemicals be applied as bioactive antidiabetic agents. Their mode actions from regulating intracellular reactive oxygen species (ROS) which cause pro-inflammatory (OS) DM. Besides, they a great potential control epigenetic mutations hyperglycemia help back blood glucose normal level. Therefore, current review addresses important role functional antioxidants management its association with OS complications.

Language: Английский

Citations

4
Integrating Metabolomic Analysis, Network Pharmacology, and Molecular Docking to Underlying Pharmacological Mechanism and Ethnobotanical Rationalization for Diabetes Mellitus: Study on Medicinal Plant Fibraurea tinctoria Lour. DOI Open Access
Abdul Halim Umar,

Septina Asih Widuri,

Yohana Caecilia Sulistyaningsih

et al.

Phytochemical Analysis, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 13, 2024

Fibraurea tinctoria Lour. has long been used in traditional medicine to treat diabetes mellitus (DM). However, a comprehensive scientific understanding of its potential active compounds and underlying pharmacological mechanisms still needs be unveiled.

Language: Английский

Citations

0
Classification of diabetes mellitus disease at Rato Ebuh Hospital-Indonesia using the K-Nearest neighbors method based on missing value DOI Creative Commons
Sigit Susanto Putro,

Moh Abdan Syakura Putra,

Doni Abdul Fatah

et al.

BIO Web of Conferences, Journal Year: 2024, Volume and Issue: 146, P. 01081 - 01081

Published: Jan. 1, 2024

Diabetes mellitus is a chronic disease often caused by high blood glucose levels and insufficient insulin production. This research aims to address the classification problem of diabetes using K-Nearest Neighbor (K-NN) method. The aim this create machine learning model that can detect early. study was conducted at Syarifah Ambami Rato Ebu Hospital in Bangkalan, utilizing data from 120 patients 2019, employing mining techniques classify patients. Additionally, steps involve determining significant variables or features for Cleansing normalization transformation. compares training test results with ratios 90:10, 80:20, 70:30. Experimental show K-NN neighbor value K=11 achieves highest accuracy rate 83% reduced error 16.67%, AUC 0.7407. These indicate 90:10 split ratio yields best performance terms class differentiation mellitus, as well lowest compared other ratios. provides better understanding demonstrates effective addressing problems, focusing on specific influence disease. Therefore, it be concluded suitable algorithm classifying mellitus.

Language: Английский

Citations

0