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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) IT JOURNAL RESEARCH AND DEVELOPMENT
Salsabila, Sona Regina
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Low-Cost Early Detection Device for Breast Cancer based on Skin Surface Temperature Arsyad Cahya Subrata; Sirajuddin, Muhammad Mar’ie; Salsabila, Sona Regina; Ibad, Irsyadul; Prasetyo, Eko; Yusmianto, Ferry
IT Journal Research and Development Vol. 9 No. 1 (2024)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2024.16034

Abstract

One of the deadly diseases that attacks many women is breast cancer. It was recorded that breast cancer cases in 2020 were 2.3 million, with deaths accounting for 29% of these cases. The BSE technique is an easy way of early identification of breast cancer that can be done independently. However, this technique often goes wrong when practiced, making it ineffective. An early breast cancer detection system is proposed to make it easier for women to carry out early identification independently. Detection is carried out based on the measured temperature of the breast surface. The temperature difference at each point is a reference for the potential for breast cancer. This system was built in a bra and tested with a mannequin as a simulator subject. The MLX90614 temperature sensor, as the primary sensor, succeeded in measuring the surface temperature of the dummy with 99% accuracy. Final testing of the proposed system can also differentiate the temperature differences in each zone.
Machine Learning-Based Early Breast Cancer Detection Through Temperature and Color Skin with Non-Invasive Smart Device Salsabila, Sona Regina; Surono, Sugiyarto; Ibad., Irsyadul; Prasetyo, Eko; Subrata, Arsyad Cahya; Thobirin, Aris
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 4 (2024): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i4.30340

Abstract

Breast cancer remains a significant global health issue, affecting millions of women and often leading to late-stage diagnoses. Traditional diagnostic methods, such as mammograms, ultrasounds, and biopsies, are effective but can be costly, invasive, and not widely accessible, causing delays in detection and treatment.  This research highlights the potential of using machine learning models with physiological data for early breast cancer detection. By capturing subtle physiological variations from a smart bra, the device allows real-time, non-invasive monitoring, offering a preventive solution that reduces the need for frequent clinical visits. The data were collected from a modified mannequin designed to simulate conditions related to breast cancer. To classify cancerous conditions based on temperature and color data, three machine learning models were evaluated.  The Random Forest (RF) model proved to be the most effective, achieving 89% accuracy, 86.11% precision, 88.57% recall, and an F1-score of 87.33%, demonstrating strong performance in identifying complex patterns. The Support Vector Machine (SVM) achieved an accuracy of 81.25%, precision of 85.7%, recall of 80%, and an F1-score of 82.64%. The Multilayer Perceptron (MLP) exhibited an accuracy of 72%, precision of 69.69%, recall of 65.71%, and an F1-score of 67.52%, suggesting potential but requiring further optimization.  These models serve as valuable tools to assist medical professionals in early screening efforts. Future research should aim to improve the models’ generalizability by expanding the dataset, utilizing data augmentation, applying transfer learning, and incorporating additional variables. Clinical validation and human trials are essential next steps to evaluate the system's effectiveness.
Co-Authors Aris Thobirin, Aris Arsyad Cahya Subrata Eko Prasetyo Heni Pujiastuti Ibad, Irsyadul Ibad., Irsyadul Sirajuddin, Muhammad Mar’ie Subrata, Arsyad Cahya Sugiyarto Surono, Sugiyarto Yusmianto, Ferry