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Journal of Electronics, Electromedical Engineering, and Medical Informatics
Published by Polilteknik Kesehatan Kemenkes Surabaya
ISSN : -     EISSN : 26568632     DOI : https://doi.org/10.35882/jeeemi
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
The Journal of Electronics, Electromedical Engineering, and Medical Informatics (JEEEMI) is a peer-reviewed open-access journal. The journal invites scientists and engineers throughout the world to exchange and disseminate theoretical and practice-oriented topics which covers three (3) majors areas of research that includes 1) Electronics, 2) Biomedical Engineering, and 3)Medical Informatics (emphasize on hardware and software design). Submitted papers must be written in English for an initial review stage by editors and further review process by a minimum of two reviewers.
Arjuna Subject : Teknik (semua kategori) - Teknik Biomedis
Articles 25 Documents
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Search results for , issue "Vol 7 No 3 (2025): July" : 25 Documents clear
EEG Performance Signal Analysis for Diagnosing Autism Spectrum Disorder using Butterworth and Empirical Mode Decomposition Fathur Rahman, Imam; Melinda, Melinda; Irhamsyah, Muhammad; Yunidar, Yunidar; Nurdin, Yudha; Wong, W.K.; Zakaria, Lailatul Qadri
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 3 (2025): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i3.788

Abstract

Electroencephalography (EEG) is a technique used to measure electrical activity in the brain by placing electrodes on the scalp. EEG plays an essential role in analyzing a variety of neurological conditions, including autism spectrum disorder (ASD). However, in the recording process, EEG signals are often contaminated by noise, hindering further analysis. Therefore, an effective signal processing method is needed to improve the data quality before feature extraction is performed. This study applied the Butterworth Band-Pass Filter (BPF) as a preprocessing method to reduce noise in EEG signals and then used the Empirical Mode Decomposition (EMD) method to extract relevant features. The performance of this method was evaluated using three main parameters, namely Mean Square Error (MSE), Mean Absolute Error (MAE), and Signal-to-Noise Ratio (SNR). The results showed that EMD was able to retain important information in EEG signals better than signals that only passed through the BPF filtration stage. EMD produces lower MAE and MSE values than Butterworth, suggesting that this method is more accurate in maintaining the original shape of the signal. In subject 3, EMD recorded the lowest MAE of 0.622 compared to Butterworth, which reached 20.0, and the MSE value of 0.655 compared to 771.5 for Butterworth. In addition, EMD also produced a higher SNR, with the highest value of 23,208 in subject 5, compared to Butterworth, which reached only 1,568. These results prove that the combination of BPF as a preprocessing method and EMD as a feature extraction method is more effective in maintaining EEG signal quality and improving analysis accuracy compared to the use of the Butterworth Band-Pass Filter alone.
A Quantum Convolutional Neural Network for Breast Cancer Classification using Boruta and GA-Based Feature Selection with Quantum Feature Maps Pagadala, Veeranjaneyulu; B, Venkatesh; Boinapalli, Sindhu; Dhulipalla, Ramya Krishna; Annapoorna, S
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 3 (2025): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i3.792

Abstract

Accurate and computationally efficient classification systems are essential for the early detection of breast cancer, particularly when dealing with complex and high-dimensional medical datasets. Traditional machine learning models often face limitations in capturing intricate nonlinear relationships inherent in such data, potentially compromising diagnostic performance. In this study, we introduce QBG-QCNN, a Quantum-enhanced framework named Boruta-GA optimized Quantum Convolutional Neural Network, designed for breast cancer classification. The model is trained on the Breast Cancer Wisconsin (Diagnostic) Dataset, which contains 30 numerical features extracted from fine needle aspiration (FNA) images of breast tissue samples. To reduce dimensionality while preserving critical diagnostic information, a hybrid Boruta-GA feature selection strategy is applied to extract key features such as radius_mean, texture_mean, area_mean, and concavity_mean. These selected features are then encoded into a 4-qubit quantum circuit using advanced quantum feature maps ZZFeatureMap, RealAmplitudes, and EfficientSU2, eliminating the need for manual feature engineering. The encoded quantum data is processed through a QCNN that incorporates quantum convolution, pooling, and parameterized ansatz layers, leveraging quantum entanglement and parallelism for more efficient learning. Implemented using PennyLane and IBM Qiskit, and optimized with the COBYLA, the model achieves outstanding performance: 94.3% accuracy, 95.2% precision, 94.6% recall, and a 93.0% F1-score. These results significantly outperform those of classical CNNs, standard QNNs, and other hybrid models. In conclusion, QBG-QCNN demonstrates that quantum machine learning, when integrated with intelligent feature selection, offers a powerful, scalable, and interpretable solution for early-stage breast cancer diagnosis. Future research will extend this framework to multi-modal datasets and real-device deployment on real quantum devices under noise constraints.
SympTextML: Leveraging Natural Language Symptom Descriptions for Accurate Multi-Disease Prediction Dhairya Vyas; Milind Shah; Harsh Kantawala; Brijesh Patel; Patel, Tejas; Enamala, Jalaja
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 3 (2025): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i3.946

Abstract

This research presents an AI-driven framework for multi-disease classification using natural language symptom descriptions, optimized through large language model (LLM) oriented preprocessing techniques. The proposed system integrates essential NLP steps text normalization, lemmatization, and n-gram vectorization to convert unstructured clinical symptom data into machine-readable form. A publicly available dataset comprising 8,498 samples across ten common diseases, including pneumonia, heart attack, diabetes, stroke, asthma, and depression, was used for training and evaluation. Data balancing and cleaning ensured uniform class representation with 1,200 samples per disease category. The processed dataset was subjected to supervised machine learning models, including SVM, KNN, Decision Tree, Random Forest, and Extra Trees, to identify the most effective classifier. Experimental results, conducted in Google Colab, showed that ensemble models (Random Forest and Extra Trees) significantly outperformed the others, achieving 99% accuracy, precision, recall, and F1-scores, while SVM and Decision Tree followed closely with 98% performance across metrics. Notably, the models consistently predicted pneumonia with high confidence for relevant input queries , validating the framework's robustness. This work demonstrates the efficacy of integrating LLM-compatible preprocessing with traditional ML classifiers for accurate disease detection based on symptom narratives. The proposed approach serves as a foundational step toward developing scalable, intelligent healthcare support systems capable of real-time disease prediction and decision-making assistance.
Machine Learning-Based Approach for Uterine Cancer Detection and Classifier Evaluation Samarasam, Brindha; Justin, Judith
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 3 (2025): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i3.632

Abstract

The necessity of early diagnosis of abnormal cell growth is critical to support patient monitoring and earlier clinical analysis. Uterine cancer is the most common gynecological malignancy among women, with endometrial cancer being the predominant type occurring in the endometrial layer. Endometrial cancer is a commonly identified type of uterine cancer that majorly occurs in the endometrial layer. This research applies machine learning (ML) algorithms to detect uterine cancer using texture-based features extracted from medical images. Specifically, a hybrid combination of Grey Level Co-occurrence Matrix (GLCM) and Grey Level Run Length Matrix (GLRLM) properties is proposed to derive 34 features, including entropy, long-run emphasis, short-run low grey level emphasis, and high grey level run emphasis. To ensure data quality, a comprehensive dataset was collected and preprocessed, followed by the implementation of an improved approach for feature normalization and ranking. The top-ranked features were then used to train and validate multiple ML algorithms, including Adaptive Neuro-Fuzzy Inference System (ANFIS), K-Nearest Neighbor (K-NN), Linear Discriminant Analysis (LDA), Radial Basis Function (RBF), Support Vector Machine (SVM), Naïve Bayes (NB), and Artificial Neural Network (ANN). Results show that the best-performing algorithm achieves an accuracy of 97.3%, sensitivity of 96.3%, and specificity of 99.2%. The algorithm's performance was further validated using Receiver Operating Characteristics (ROC) analysis and F1 scores, both of which demonstrated superior predictive capability. Additionally, Explainable AI (XAI) techniques were integrated to elucidate the features and patterns recognized by the algorithm as indicative of endometrial carcinoma. Layer-wise relevance propagation (LRP) was employed to backtrack the neural network’s output decisions to the input features, highlighting the most influential factors in the algorithm's predictions. This research demonstrates the potential of applying ML algorithms to improve early detection of uterine cancer, offering a non-invasive, accurate, and cost-effective alternative to traditional imaging methods.
Improving Kidney Stone Detection with YOLOV10 and Channel Attention Mechanisms in Medical Imaging Bala, Saroj; Arora, Kumud; V, Satheeswaran; S, Mohan; J, Deepika; K, Sangamithrai; Doss, Amala Nirmal
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 3 (2025): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i3.868

Abstract

Accurate and timely detection of kidney stones is crucial for effective medical intervention and treatment planning. However, existing detection methods often struggle with challenges related to sensitivity, precision, and the ability to process complex and variable medical images. In this study, an advanced kidney stone detection system is developed using the latest object detection algorithm, You Only Look Once version 10 (YOLOv10), integrated with channel attention mechanisms to enhance model performance. This combination aims to improve detection accuracy by enabling the network to focus more precisely on critical regions in medical images, particularly in Computed Tomography (CT) scans, where kidney stones may appear in varying shapes, sizes, and intensities. The proposed system begins with data augmentation techniques, such as rotation, scaling, and contrast adjustments, to enhance the model’s generalization ability across different image conditions and patient profiles. YOLOv10 was selected due to its lightweight architecture, high detection speed, and enhanced performance in small object detection tasks. To further improve feature extraction, channel attention mechanisms such as Squeeze-and-Excitation (SE) blocks or Efficient Channel Attention (ECA) modules are incorporated. These modules enable the network to selectively focus on the most informative feature channels associated with kidney stone regions, while suppressing irrelevant background information, thereby improving the distinction between stones and surrounding tissues. The model is trained and fine-tuned using a diverse CT scan dataset containing various types and sizes of kidney stones. Evaluation results demonstrate that the proposed model achieves a high detection accuracy of 93.7% with a very low loss of 0.18. It exhibits stability without issues like overfitting, underfitting, or local minima entrapment, making it a highly reliable tool for clinical applications.

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