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All Journal JOIV : International Journal on Informatics Visualization Jurnal Teknik Informatika (JUTIF)
Nafea, Mohammed Mansoor
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Computer Science & IT

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Improving Extreme Gradient Boosting Model for Heart Disease Prediction Using SMOTE for Class Imbalance Rohmayani, Dini; Sugianto, Castaka Agus; Perdana, Rangga Satria; Nafea, Mohammed Mansoor
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4753

Abstract

The goal of this study is to come up with an intelligent predictive model that can classify the severity of heart disease. The model will employ both XGBoost and oversampling to resolve the problem of data imbalance. In addition, the model will be implemented for real-world application using an interactive interface. The study uses the UCI Heart Disease dataset, which includes many clinical features. Preprocessing involves handling missing values, removal of features with a substantial fraction of missing values, and the use of SMOTE resampling for learning from class-balanced instances. The main classifier that was used for the research purposes was the XGBoost classifier, while the dataset was split 80:20 for training and testing purposes. For ease of individual-level real-time testing of the predictions, the model is implemented through Streamlit. The XGBoost model worked extraordinarily well, with the accuracy standing at 92%, as did precision along with recall, as well as the F1-score, being 92%. These findings clearly outperform other current studies of the same sort that have made use of alternative classifiers. In addition, its deployment using Streamlit makes it even more clinically applicable. Innovation The novelty of the research lies in the combined application of SMOTE with XGBoost, enabling effective classification under imbalanced conditions, along with the real-time implementation using Streamlit for user-level predictions. The model is of high value for early identification and stratification of the severity of heart disease in clinical decision support settings.
An Enhanced Routing Protocol For Vehicular Ad Hoc Networks With Swarm Intelligent Tareq, Mustafa; Farhan, Yasir Hadi; Nafea, Mohammed Mansoor
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.3298

Abstract

A Vehicular Ad Hoc Network (VANET) is a transient network of wireless mobile nodes operating without centralized administration or pre-existing infrastructure. VANETs are a subset of Mobile Ad Hoc Networks (MANETs) designed to facilitate vehicular communication. This allows vehicles to communicate directly with roadside devices or with each other. These networks are appropriate for applications like infotainment services, traffic control, and accident avoidance since they are dynamic, decentralized, and highly flexible. However, their lack of established infrastructure presents serious difficulties, especially when preserving dependable routing and energy efficiency. Path selection in VANETs usually attempts to limit the number of intermediary nodes required to reach a destination to reduce latency and possible points of failure. However, as the distance between nodes increases, so does the required transmission power, directly impacting the network's energy consumption. As a result, energy-efficient routing is crucial to maintain network longevity and performance. This paper introduces the Bee Destination Sequenced Distance Vector Routing (B-DSDV) protocol, utilizing swarm intelligence principles via the Artificial Bee Colony (ABC) algorithm to enhance energy efficiency within a DSDV framework. This integration incorporates the Bee Algorithm into the discovery mechanism of DSDV to identify the most accessible node and the shortest route based on node distances. The algorithm assesses both the power levels of nodes and their distances to others. Route selection is optimized by considering the power consumption of intermediate nodes between the source and destination. Performance evaluation of the B-DSDV protocol is compared with established protocols, demonstrating its effectiveness in selecting high-power optimal paths and improving overall performance. The simulation results were conducted based on average throughput, average energy consumption, average end-to-end delay, and packet delivery ratio performance metrics. We conducted a simulation study using Network Simulator (NS) version 2.35 to evaluate the performance metrics of the routing protocols. Regarding energy consumption, the B-DSDV protocol achieved superior results, approximately 0.10% concerning packet size, compared to other protocols.
Co-Authors Farhan, Yasir Hadi Perdana, Rangga Satria Rohmayani, Dini Sugianto, Castaka Agus Tareq, Mustafa