Journal of Electronics, Electromedical Engineering, and Medical Informatics

Heart Disease Classification Using Random Forest and Fox Algorithm as Hyperparameter Tuning

Afidatul Masbakhah, Umu Sa'adah, Mohamad Muslikh — Fri, 01 Aug 2025 08:23:02 +0700

Heart disease remains the leading cause of death worldwide, making early and accurate diagnosis crucial for reducing mortality and improving patient outcomes. Traditional diagnostic approaches often suffer from subjectivity, delay, and high costs. Therefore, an effective and automated classification system is necessary to assist medical professionals in making more accurate and timely decisions. This study aims to develop a heart disease classification model using Random Forest, optimized through the FOX algorithm for hyperparameter tuning, to improve predictive performance and reliability. The main contribution of this research lies in the integration of the FOX metaheuristic optimization algorithm with the RF classifier. FOX, inspired by fox hunting behavior, balances exploration and exploitation in searching for the optimal hyperparameters. The proposed RF-FOX model is evaluated on the UCI Heart Disease dataset consisting of 303 instances and 13 features. Several preprocessing steps were conducted, including label encoding, outlier removal, missing value imputation, normalization, and class balancing using SMOTE-NC. FOX was used to optimize six RF hyperparameters across a defined search space. The experimental results demonstrate that the RF-FOX model achieved superior performance compared to standard RF and other hybrid optimization methods. With a training accuracy of 100% and testing accuracy of 97.83%, the model also attained precision (97.83%), recall (97.88%), and F1-score (97.89%). It significantly outperformed RF-GS, RF-RS, RF-PSO, RF-BA, and RF-FA models in all evaluation metrics. In conclusion, the RF-FOX model proves highly effective for heart disease classification, providing enhanced accuracy, reduced misclassification, and clinical applicability. This approach not only optimizes classifier performance but also supports medical decision-making with interpretable and reliable outcomes. Future work may involve validating the model on more diverse datasets to further ensure its generalizability and robustness.

Hybrid CNN–ViT Model for Breast Cancer Classification in Mammograms: A Three-Phase Deep Learning Framework

Vandana Saini, Meenu Khurana, Rama Krishna Challa — Thu, 07 Aug 2025 00:00:00 +0700

Breast cancer is one of the leading causes of death among women worldwide. Early and accurate detection plays a vital role in improving survival rates and guiding effective treatment. In this study, we propose a deep learning-based model for automatic breast cancer detection using mammogram images. The model is divided into three phases: preprocessing, segmentation, and classification. The first two phases, image enhancement and segmentation, were developed and validated in our previous works. Both phases were designed in a robust manner using learning networks; the usage of VGG-16 in preprocessing and U-net in segmentation helps in enhancing the overall classification performance. In this paper, we focus on the classification phase and introduce a novel hybrid deep learning based model that combines the strengths of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). This model captures both fine-grained image details and the broader global context, making it highly effective for distinguishing between benign and malignant breast tumors. We also include attention-based feature fusion and Grad CAM visualizations to make predictions more explainable for clinical use and reference. The model was tested on multiple benchmark datasets, DDSM, INbreast, and MIAS, and a combination of all three datasets, and achieved excellent results, including 100% accuracy on MIAS and over 99% accuracy on other datasets. Compared to recent deep learning models, our method outperforms existing approaches in both accuracy and reliability. This research offers a promising step toward supporting radiologists with intelligent tools that can improve the speed and accuracy of breast cancer diagnosis.

Optimizing Medical Logistics Networks: A Hybrid Bat-ALNS Approach for Multi-Depot VRPTW and Simultaneous Pickup-Delivery

This paper tackles the multi-depot heterogeneous-fleet vehicle-routing problem with time windows and simultaneous pickup and delivery (MDHF-VRPTW-SPD), a variant that mirrors he growing complexity of modern healthcare logistics. The primary purpose of this study is to model this complex routing problem as a mixed-integer linear program and to develop and validate a novel hybrid metaheuristic, B-ALNS, capable of delivering robust, high-quality solutions. The proposed B-ALNS combines a discrete Bat Algorithm with Adaptive Large Neighborhood Search, where the bat component supplies frequency-guided diversification, while ALNS adaptively selects destroy and repair operators and exploits elite memory for focused intensification. Extensive experiments were conducted on twenty new benchmark instances (ranging from 48 to 288 customers), derived from Cordeau’s data and enriched with pickups and a four-class fleet. Results show that B-ALNS attains a mean cost 1.15 % lower than a standalone discrete BA and 2.78 % lower than a simple LNS, achieving the best average cost on 17/20 instances and the global best solution in 85% of test instances. Statistical tests further confirm the superiority of the hybrid B-ALNS, a Friedman test and Wilcoxon signed-rank comparisons give p-value of 0.0013 versus BA and p-value of 0.0002 versus LNS, respectively. Although B-ALNS trades speed for quality (182.65 seconds average runtime versus 54.04 seconds for BA and 11.61 seconds for LNS), it produces markedly more robust solutions, with the lowest cost standard deviation and consistently balanced routes. These results demonstrate that the hybrid B-ALNS delivers statistically significant, high-quality solutions within tactical planning times, offering a practical decision-support tool for secure, cold-chain-compliant healthcare logistics