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Journal : CogITo Smart Journal

Pemanfaatan Algoritma Machine Learning dan Long-Short Term Memory untuk Prediksi Dini Diabetes Yuri Pamungkas; Meiliana Dwi Cahya; Endah Indriastuti
CogITo Smart Journal Vol. 10 No. 1 (2024): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v10i1.630.491-506

Abstract

Diabetes, a chronic condition, affects numerous populations. Poor insulin production from the pancreas combined with high blood sugar levels can result in the onset of diabetes. Diabetes can be caused by numerous factors. Observe and prevent these factors to reduce the high prevalence of diabetes. This study concentrates on medical record data for determining diabetes risk factors via statistical correlation analysis. These factors will be utilized as machine learning and LSTM input parameters for diabetes prediction. The factors analyzed include blood glucose levels, HbA1c levels, age, BMI, hypertension, heart disease, smoking habits, and gender. Based on the research results, we found that glucose levels (>137 mg/dL) and HbA1c levels (>6.5%) are the main benchmarks in diagnosing diabetes. It is also supported by the correlation value, which is relatively high (0.42 and 0.40, respectively) compared to other factors. Increasing age and BMI also increase the risk of developing diabetes. Comorbidities (such as hypertension or heart disease) and smoking habits can worsen the condition of people with diabetes. Meanwhile (based on gender), women are more at risk of developing diabetes than men because their body mass index increases during the monthly cycle. Apart from that, there is a tendency for blood sugar levels in women to increase in the last two weeks before menstruation. Based on the prediction results, the highest levels of accuracy, sensitivity, and F1 score were obtained (96.97%, 99.97%, and 98.37%) using the LSTM method. This performance shows that LSTM is relatively good for the diabetes prediction process based on existing factors/parameters.
Co-Authors Abdul Karim Abdurahman Abdurrahman Achmad Syaifudin Adhi Dharma Wibawa Adhi Dharma Wibawa, Adhi Dharma Ahmadinejad, Iqbal Aisar, Muhammad Alfonsus Haryo Sangaji Arjunnaja, Moch. Aung, Myo Min Balqis, Dayana Satira Cahya, Meiliana Dwi Derek, Natan Dian Puspita Hapsari Djaputra, Edith Maria Dwinka Syafira Eljatin Elfrida Ratnawati Evi Triandini Fadli, Sonny Faiqoh, Elok Nur Fitriani, Fatimah Nur Forca, Adrian Jaleco Ginting, Tsamarah Amelia Putri Hashim, Uda Haykal, Muhammad Nazhif Haykal, Muhammad. Najib Hedianto, Tri Hidayah, Rizka Nurul Hisbiyah, Yuni Imam Susilo Indriani, Ratri Dwi Indriastuti, Endah Indriastuti, Endah Jafari, Nadya Paramitha Kamil, Ihtifazhuddin Fathul Karimah, Rumman Kendenan, Valentino Kuswanto, Djoko Larasati, Alya Puti Made Krisnanda Meiliana Dwi Cahya Meiliana Dwi Cahya Midzkar, Wuli Silan Mubarok, Fahmi Muhammad Faizi, Muhammad Mukhairiq, Gusfatul Nakkliang, Kanittha Njoto, Edwin Nugroho Nugroho Njoto, Edwin Nur Rochmah, Nur Nur, R. Rossa Alfi Nur, Rossa Alfi Padma Nyoman Crisnapati Parlindungan, Putra Gelar Perwitasari, Rayi Kurnia Pratasik, Stralen Prawesti, Indyra Yudha Putra, Gumilar Fardhani Ami Putri Alief Siswanto Putri, Atina I.W. Putri, Atina Irani Wira Putri, Ziyan Nadia Putu Adi Guna Permana Rachmadiana, Josephine Larissa Radiansyah, Riva Satya Rais, Bryan Ramadani, Muhammad Rifqi Nur Rangkuti, Rahmah Yasinta Ridhoi, Ahmad Risald, Randi Achtiar Risaldi, Randi Achtiar Sabilla, Shoffi Izza Sain, Anabela Aulia Sakina Sangsawang, Thosporn Shofwan Hanief Supeno Mardi Susiki Nugroho, Supeno Mardi Syafira Eljatin, Dwinka Syulthoni, Zain Budi Tawakkal, Raihan Achmad Thwe, Yamin Uda, Muhammad Nur Afnan Wawan Yunanto Yulan, Gao