Intelligent Prediction and Detection of Diabetes Mellitus Using Machine Learning - Real-time blood sugar classification and telemonitoring

Aug 2, 2026

Diabetes Mellitus affects a large and growing number of people, and delayed diagnosis together with difficulties in monitoring blood sugar levels increases disease aggravation. The presented approach builds an intelligent blood sugar monitoring system to help patients check glucose levels regularly while supporting prediction and detection. Machine learning is used to analyze, predict, and classify health data, enabling real-time blood sugar level classification. Results indicate the system can monitor glucose measurement outcomes and support telemonitoring and telediagnosis workflows.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Introduction

Problem background and need for monitoring

Proposed IoT and Android-based monitoring system

Related Works

IoT-based medical data collection

Rule-based and machine learning diabetes classification

Intelligent Prediction and Detection of Diabetes Mellitus Using Machine Learning S Handoko1, Sukamto2, L Triyono3, I Hestiningsih 4, E Sato-Shimokarawa*5, E ELavindi6 1,2,3,4,6 Politeknik Negeri Semarang, Indonesia 5 Tokyo Metropolitan University, Japan [E-mail: kang.handoko@polines.ac.id](E-mail: kang.handoko@polines.ac.id1)[1](E-mail: kang.handoko@polines.ac.id1), [sukamto@polines.ac.id](sukamto@polines.ac.id2)[2](sukamto@polines.ac.id2), [liliek.triyono@polines.ac.id](liliek.triyono@polines.ac.id3)[3](liliek.triyono@polines.ac.id3), [hestidha@polines.ac.id](hestidha@polines.ac.id4)[4](hestidha@polines.ac.id4), [eri@tmu.ac.jp](eri@tmu.ac.jp5)[5](eri@tmu.ac.jp5), [sukotyasp@polines.ac.id](sukotyasp@polines.ac.id6)[6](sukotyasp@polines.ac.id6) Submitted: 4 January 2023, revised: 12 July 2023, accepted: 25 August 2023 Abstract. One of the diseases with a fairly high number of sufferers today is Diabetes Mellitus. The increase in the number of people with diabetes is caused by diagnosis delays and difficulties in monitoring the blood sugar level. Therefore, a solution is needed to overcome this problem: a blood sugar level monitoring system to predict and detect. The blood sugar level monitoring system is an intelligent system that can monitor blood sugar levels in Diabetes Mellitus patients. This system aims to make it easier for patients to check blood sugar levels regularly, to minimize the occurrence of increased blood sugar levels that aggravate the disease. Moreover, machine learning algorithms are a viable method used in recent studies for analyzing, predicting, and classifying health data while improving the health conditions of telemonitoring and telediagnosis. The main purpose of this article is to employ machine learning algorithms for real-time blood sugar level classification. The results of this study indicate that the system can be used to monitor blood sugar levels. The results of the system implementation that users can use include monitoring the results of measuring blood sugar levels. Keywords: monitoring machine learning, prediction, diabetes mellitus, data mining. 1. Introduction Diabetes Mellitus is a disease caused by chronic metabolic disorders with various etiologies characterized by high blood sugar levels accompanied by impaired carbohydrate, lipid, and protein metabolism as a result of insulin function insufficiency [1] . IDF (International Diabetes Federation) states that around 19.46 million people in Indonesia have diabetes in 2021, which shows an increase percentage of 81.8% from 2019. Based on that record, Indonesia is the fifth country with the most diabetes cases in the world after China, India, Pakistan, and the United States (US) . Indonesia is the only country in Southeast Asia in the top 10 countries with the most cases of diabetes [2] . The increase in the number of people with diabetes is caused by delays in diagnosis, unhealthy lifestyle, and the difficulty of monitoring during the treatment period [3] . Therefore, a solution is needed to overcome this problem, namely a blood sugar level monitoring system. Currently, many use invasive techniques or take a patient's blood sample for later processing in the laboratory to measure glucose levels in the blood. This causes many diabetic people hesitate to check their glucose levels regularly [4] . For that, a tool to check blood sugar levels that are carried out without injuring the body is needed, namely non-invasive techniques. Based on the problems that have been stated above, an IoT and Android™-based blood sugar level monitoring system for diabetic people is proposed. The blood sugar level monitoring system is an intelligent system that can monitor blood sugar levels in Diabetes Mellitus patients. The purpose of this proposed system is to make it easier for patients to check their blood sugar levels regularly to minimize the occurrence of an increase in blood sugar levels, which aggravates the disease. This system wa

FAQs

Why is diabetes monitoring important, and what problem does the work address?

The work targets delayed diagnosis and difficulty monitoring blood sugar during treatment. These issues can lead to increased glucose levels that worsen diabetes.

How does the proposed system monitor and classify blood sugar levels?

It uses an IoT-based blood sugar level monitoring system integrated with Android. Machine learning algorithms support real-time blood sugar level classification.

Why is the approach considered suitable compared with invasive glucose testing?

The document notes that invasive techniques require blood samples and can make patients hesitate to monitor regularly. It therefore motivates non-invasive monitoring supported by IoT and mobile delivery of results.

Aug 2, 2026

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