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All Journal Medical Journal of Indonesia Jurnal Kesehatan Medika Saintika Jurnal Medika Malahayati Jurnal Pengabdian Kepada Masyarakat Ungu ( Abdi Ke Ungu) Journal of Applied Data Sciences
Taufik Jamaan
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Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Health Professions Immunology & microbiology Medicine & Pharmacology Nursing Public Health Other

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Journal : Journal of Applied Data Sciences

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Multi-Algorithm to Measure the Accuracy Level of Diabetes Status Prediction Zulkifli, Zulkifli; Makkiyah, Feda Anisah; Antoni, Darius; Fitriana, Fitriana; Jamaan, Taufik; Taufik, Ahmad
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.250

Abstract

Poor management of diabetes leads to damage in organs and body tissues, impacting crucial organs like the heart, kidneys, eyes, and nerves. Although there is no permanent cure for diabetes, early detection enables effective disease management, which researchers and medical professionals agree enhances recovery prospects. The rapid progress in information technology has facilitated early prediction and diagnosis of diseases through Machine Learning (ML), a subset of Artificial Intelligence (AI) comprising various algorithms such as Neural Network, Support Vector Machine (SVM), kNN, Random Forest, and Naïve Bayes. These algorithms serve as effective tools in handling predictive data. Early prediction of diabetes holds the potential to control the disease and save lives. Therefore, the focus of this research is to develop a predictive model for diabetes status by utilizing various algorithms, but the level of validation of this model still needs to be tested. The dataset utilized consists of information from several diabetic patients, including eight input variables (pregnancies, glucose levels, blood pressure, skin thickness, insulin levels, BMI, age, and diabetes pedigree function) and one output variable (diabetes status). Research findings indicate that the SVM algorithm exhibits superior accuracy (84%) in predicting diabetes status compared to other algorithms such as neural network, Random Forest, Naïve Bayes, and kNN.
Co-Authors Ahmad Taufik Antoni, Darius Arief Boediono Ariwibowo, Catur Desi Kumalasari Eka Tri Wulandari, Eka Tri Farid A. Moeloek Fitriana Fitriana Heni Pujiastuti Juwita Desri Ayu LINDA PUSPITASARI - A11111054, LINDA PUSPITASARI Makkiyah, Feda Anisah Mareza Yolanda Umar Psiari Kusuma Wardani Retno, Sri Nowo Rikma Ratri, Arda Rini Wahyuni Ririn Wulandari rosita, meli Siti Maesaroh Siti Rohani SRI RAHAYU Sukarni Sukarni Tisnawati, Riya Trevino A. Pakasi