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Makkiyah, Feda Anisah
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Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Immunology & microbiology Law, Crime, Criminology & Criminal Justice Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Public Health Social Sciences 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 Abd. Rasyid Syamsuri Ahmad Aulia Ahmad Taufik Aisyah, Ammara Alifah Safira Amperatmoko Alya Roza Lisanti Anisah Anisah Antoni, Darius Apriningsih, Apriningsih Apriningsih Arfiyanti Arfiyanti Arfiyanti, Arfiyanti Aritonang, Hosianna Stephany Ayu Devania Dipta Gayatri, Gusti Ayu Magdalena Natalia Situmeang Basuki Supartono Bintang Bayu Aryandi Buana, Gherriandi Raja Budiman, Anisa Novita Dian Pertiwi Dian Pertiwi Digna, Audrey Alvura Erna Harfiani Faustine, Gracella Fitriana Fitriana Goenarjo, Roman Gracella Faustine Heni Pujiastuti Heristyorini, Ayodya Herkan, Muhamad Thariq Imam Prabowo, Imam Juliana, Dian Kamaludin, Fahmi Marlina Dewiastuti Marlina Dewiastuti, Marlina Masiran, Ruziana` Meiskha Bahar Melly Kristanti Muhammad Ferrel Privansyah Cader Cader Muhammad Reihan Arianda Nabila Aulia Ainaya Nafira, Amodya Dahayu Napitupulu, Partogi Natanel, Handro Steve Nugrahana, Raisa Yunika Nurfitri Bustamam Nurhadi Ibrahim Nurrahmah, Rahma Nida Nurrizka, Rahmah Huda Oktafiani, Devi Oktavia, Sabina Pasiak, Taufiq Fredrik Prita Maya Savitri Rachmawati, Alfira Rahma Hida Nurrizka Rahmah Hida Nurrizka Rahmah Huda Nurrizka Rizkyta, Bunga Mentari Rosadi, Andrea Salsabila Sabita, Rifqi Safira, Lisa Salim Harris, Salim Sembiring, Rinawati Setyaningsih, Yuni Shelly Nobel Shofiy, Alifya Rayyani Sihotang, Theodora Caroline Subeno, Setia Syahrin, Rosita Alfi Taufik Jamaan Taufiq Fredrik Pasiak Tiwuk Susantiningsih Wahyudi, Dhyani Paramita Widayani Wahyuningtyas Yani, Sri Yuni Setyaningsih Yuri Nurdiantami Zulfa , Fajriati