article · BIMA JOURNAL OF SCIENCE AND TECHNOLOGY GOMBE
Predicting cancer progression often relies on single data sources, which miss the benefits of combining different diagnostic inputs. A multimodal machine learning framework addresses this limitation by joining plain radiographs with clinical patient records. The approach couples deep learning techniques to evaluate medical imaging with interpretable models designed to assess clinical variables. In testing, this integrated model reached an overall accuracy of 94 percent, alongside a training accuracy of 98.01 percent, notably surpassing unimodal baselines such as standalone support vector machines and convolutional neural networks. It also recorded 94.2 percent precision, 94 percent recall, and an area under the receiver operating characteristic curve of 98 percent. By successfully fusing complementary data streams, the framework enhances predictive performance and offers a robust basis to assist clinical decision-making and personalised treatment strategies.
Cancer progression can be difficult to forecast using individual diagnostic tests alone. By uniting routine X-rays and patient health records into a single computational system, clinicians gain a more comprehensive diagnostic tool. This integrated approach improves prognostic accuracy, helping medical teams identify high-risk cases earlier and tailor treatment plans to individual patient needs.
This technology offers an algorithmic foundation for clinical decision-support software aimed at oncologists and hospital radiology departments. By predicting cancer progression through routine radiographs and patient data, it could integrate into hospital health information systems. However, as the abstract presents performance metrics from experimental testing without describing clinical trials or regulatory compliance, the system appears to be at an early-stage research or applied prototype level rather than near-market deployment.
AI-generated from the published abstract. Always read the original work before citing.
This research investigates the use of a multimodal machine learning model to predict cancer progression by integrating radiographs and clinical data. The study addresses the limitations of unimodal approaches, which often overlook the synergistic potential of combining diverse data types. By leveraging deep learning techniques for image analysis and interpretable models for clinical data, the proposed framework enhances prediction accuracy and model interpretability. The multimodal model achieved a high training accuracy of 98.01% and a testing accuracy of 94%, significantly outperforming unimodal models like SVM and CNN. Precision (94.2%) and recall (94%) highlighted the model's ability to accurately identify true positive cases, while the AUC-ROC of 98% underscored its robust diagnostic capability. Comprehensive evaluation demonstrated that the multimodal model effectively integrates complementary data, improving predictive performance and supporting personalised treatment planning. The research contributes to advancing cancer diagnosis and prognosis, offering a promising tool for clinical decision-making.
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DOI: 10.64290/bima.v9i1a.900
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