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Tris Eryando
Departemen Biostatistik Dan Ilmu Kependudukan Fakultas Kesehatan Masyarakat Universitas Indonesia
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Journal : Berita Kedokteran Masyarakat

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Applied Machine Learning for Early Diabetes Detection Based on Symptoms Intansari; Tris Eryando; Miftakul Fira Maulidia; Edi Utomo Putro
BKM Public Health and Community Medicine The 12th UGM Public Health Symposium
Publisher : Universitas Gadjah Mada

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Abstract

Purpose: Diabetes is a chronic disease that occurs either when the pancreas does not produce enough insulin or when the body cannot effectively use the insulin it produce. Diabetes is often referred to as a silent killer because this disease can affect all organs of the body and cause various symptoms. About 422 million people worldwide have diabetes, the majority living in low-and middle-income countries, and 1.5 million deaths are directly attributed to diabetes each year. Early diabetes detection is essential to prevent serious complications in patients based on symptoms. Method: This study present a prediction using various Machine Learning (ML) algorithm based on age, gender and symptoms as predictor such as polyuria, feeling thirsty, easy itching, losing weight unintentionally, blurred vision, irritability and feeling tired. We have used such a dataset of 520 patients, which has been collected using direct questionnaires from the patients of Sylhet Diabetes Hospital, Bangladesh. Results: This study compared several machine learning algorithms such as Logistic Regression, Naive Bayes, Classification and Regression Trees (CART), K-Nearest Neighbour, and Random Forest to develop diabetes prediction model. Several parameter, including classification accuracy (CA), F1 score, precision, and recall were used to evaluate the models. CART algorithm showed better parameter values, with CA 97,1%, recall 0.953, precision 0.932, and F1 score 0.901. Conclusion: The use of machine learning models for early detection of diabetes with an accuracy rate of 97,1%. ML offers the ability to develop a quick prediction model for diabetes screening based on symptoms. We hope that with this study can contribute to the wider community by decrease the incidence of diabetes through recognizing suspicious symptoms. To prevent diabetes the future this machine learning model can be developed into a mobile application that the public can widely access.
Co-Authors Aenaya Delavera Aenaya Delavera Afriansyah, Eddy Agung Waluyo Al Asyary Al Asyary Aldila Riznawati Aldila Riznawati Allenidekania Allenidekania Apriningrum, Nelly Aria Kusuma Aria Kusuma Arief Kurniawan Nur Prasetyo Arman Harahap Artha Prabawa Astuti Yuni Nursasi Bagus, Nurzahara Bahar, Ryza Jazid Budi Anna Keliat Budiharsana, Meiwita Carol Clark Clark, Carol Daniah Daniah Delavera, Aenaya Delfiyanti, Rani Deny Yudhistira Deny Yudhistira Dera Alfiyanti Dewi Susanna Dia Wulandari Dian Kistiani Irawaty Dian Pratiwi Dian Pratiwi Dian Pratiwi Dian Pratiwi Doni Lasut Doria, Magda Dwi Prihatin Era Edi Utomo Putro Edwin van Teijlingen Efi Trimuryani Elly Nurachmah Elysabeth Sinulingga Fajar Nugraha Falupi, Lilik Aryani Gerke, Solvay Gustina, Ira Hanny Handiyani Helmi Safitri Hermansyah, Hendra Indah Sri Wahyuni Intansari Irawaty, Dian Kristiani Jesa Nuhgroho Kemal N. Siregar Makful, Martya Rahmaniati Martya Rahmaniati Martya Rahmaniati Martya Rahmaniati Martya Rahmaniati Martya Rahmaniati Meiwita Budiharsana Miftakul Fira Maulidia Milla Herdayati, Milla Nani Nurhaeni Negari, Nurfatia Nuhgroho, Jesa Nur Asniati Djaali Nurfatia Negari Nurhidayah, Nurhidayah Nuridzin, Dion Zein Prasetyo, Arief Kurniawan Nur Purnawan Junadi Purnawan Junadi Purnawan Junadi Purwantyastuti Purwantyastuti Purwantyastuti Purwantyastuti Purwantyastuti Purwantyastuti Rahmadewi Rahmadewi Ratna Sitorus Resti Sintya Ervina Restu Apriena Putri Restu Apriena Putri Retnowati Retnowati Riris Dian Hardiani Ristina Rosauli Harianja Riznawati, Aldila Roma Tao Toba MR Ryza Jazid Safanta, Nurzalia Safitri, Helmi Saini, Izzatul Mardiah Sipahutar, Tiopan Solly Aryza Solvay Gerke Sri Yona Sulastri Sulastri Supriyadi Supriyadi Talib, Suprohaita Rusdi Teijlingen, Edwin van Tiopan Sipahutar Tiopan Sipahutar Tri Agustini Trivalni, Ratih Violila, Vallery Warendi Warendi Winarni Naweng Triwulandari Winnie Tunggal Mutika Yati Afiyanti Yudhistira, Deny Yulia Herawati Yvonne M. Indrawani