Using fractal dimension to predict the risk of intra cranial aneurysm rupture with machine learning

• URL: http://arxiv.org/abs/2410.00121v1
• Date: Mon, 30 Sep 2024 18:02:09 GMT
• Title: Using fractal dimension to predict the risk of intra cranial aneurysm rupture with machine learning
• Authors: Pradyumna Elavarthi, Anca Ralescu, Mark D. Johnson, Charles J. Prestigiacomo,
• Abstract summary: Intracranial aneurysms (IAs) that rupture result in significant morbidity and mortality. Machine learning (ML) models offer the potential to provide more accuracy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Intracranial aneurysms (IAs) that rupture result in significant morbidity and mortality. While traditional risk models such as the PHASES score are useful in clinical decision making, machine learning (ML) models offer the potential to provide more accuracy. In this study, we compared the performance of four different machine learning algorithms Random Forest (RF), XGBoost (XGB), Support Vector Machine (SVM), and Multi Layer Perceptron (MLP) on clinical and radiographic features to predict rupture status of intracranial aneurysms. Among the models, RF achieved the highest accuracy (85%) with balanced precision and recall, while MLP had the lowest overall performance (accuracy of 63%). Fractal dimension ranked as the most important feature for model performance across all models.

Related papers

• Feature selection strategies for optimized heart disease diagnosis using ML and DL models [4.863856267150165]
  This study evaluates the impact of feature selection techniques on the predictive performance of various machine learning (ML) and deep learning (DL) models. Eleven ML/DL models were assessed using metrics such as precision, recall, AUC score, F1-score, and accuracy. Results indicate that MI outperformed other methods, particularly for advanced models like neural networks.
  arXiv  Detail & Related papers  (2025-03-20T09:59:01Z)
• Efficient Brain Tumor Classification with Lightweight CNN Architecture: A Novel Approach [0.0]
  Brain tumor classification using MRI images is critical in medical diagnostics, where early and accurate detection significantly impacts patient outcomes. Recent advancements in deep learning (DL) have shown promise, but many models struggle with balancing accuracy and computational efficiency. We propose a novel model architecture integrating separable convolutions and squeeze and excitation (SE) blocks, designed to enhance feature extraction while maintaining computational efficiency.
  arXiv  Detail & Related papers  (2025-02-01T21:06:42Z)
• Prediction of Lung Metastasis from Hepatocellular Carcinoma using the SEER Database [0.9055332067000195]
  Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality. predictive models for lung metastasis inHCC remain limited in scope and clinical applicability. We develop and validate an end-to-end machine learning pipeline using data from the Surveillance, Epidemiology, and End Results (SEER) database.
  arXiv  Detail & Related papers  (2025-01-20T20:06:31Z)
• Machine learning for cerebral blood vessels' malformations [38.524104108347764]
  Cerebral aneurysms and arteriovenous malformations are life-threatening hemodynamic pathologies of the brain. Parameters of cerebral blood flow could potentially be utilized in machine learning-assisted protocols for risk assessment and therapeutic prognosis.
  arXiv  Detail & Related papers  (2024-11-25T12:58:00Z)
• Advancements In Heart Disease Prediction: A Machine Learning Approach For Early Detection And Risk Assessment [0.0]
  This paper comprehends, assess, and analyze the role, relevance, and efficiency of machine learning models in predicting heart disease risks using clinical data. The Support Vector Machine (SVM) demonstrates the highest accuracy at 91.51%, confirming its superiority among the evaluated models in terms of predictive capability.
  arXiv  Detail & Related papers  (2024-10-16T22:32:19Z)
• Optimizing Mortality Prediction for ICU Heart Failure Patients: Leveraging XGBoost and Advanced Machine Learning with the MIMIC-III Database [1.5186937600119894]
  Heart failure affects millions of people worldwide, significantly reducing quality of life and leading to high mortality rates. Despite extensive research, the relationship between heart failure and mortality rates among ICU patients is not fully understood. This study analyzed data from 1,177 patients over 18 years old from the MIMIC-III database, identified using ICD-9 codes.
  arXiv  Detail & Related papers  (2024-09-03T07:57:08Z)
• Machine Learning for ALSFRS-R Score Prediction: Making Sense of the Sensor Data [44.99833362998488]
  Amyotrophic Lateral Sclerosis (ALS) is a rapidly progressive neurodegenerative disease that presents individuals with limited treatment options. The present investigation, spearheaded by the iDPP@CLEF 2024 challenge, focuses on utilizing sensor-derived data obtained through an app.
  arXiv  Detail & Related papers  (2024-07-10T19:17:23Z)
• Re-DiffiNet: Modeling discrepancies in tumor segmentation using diffusion models [1.7995110894203483]
  We introduce a framework called Re-Diffinet for modeling the discrepancy between the outputs of a segmentation model like U-Net and the ground truth. The results show an average improvement of 0.55% in the Dice score and 16.28% in HD95 from cross-validation over 5-folds.
  arXiv  Detail & Related papers  (2024-02-12T01:03:39Z)
• Penalized Deep Partially Linear Cox Models with Application to CT Scans of Lung Cancer Patients [42.09584755334577]
  Lung cancer is a leading cause of cancer mortality globally, highlighting the importance of understanding its mortality risks to design effective therapies. The National Lung Screening Trial (NLST) employed computed tomography texture analysis to quantify the mortality risks of lung cancer patients. We propose a novel Penalized Deep Partially Linear Cox Model (Penalized DPLC), which incorporates the SCAD penalty to select important texture features and employs a deep neural network to estimate the nonparametric component of the model.
  arXiv  Detail & Related papers  (2023-03-09T15:38:16Z)
• EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer [58.720142291102135]
  We propose an efficient and light-weighted learning architecture to classify and segment breast tumors simultaneously. We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions. The accuracy, sensitivity, and specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively.
  arXiv  Detail & Related papers  (2022-01-13T05:24:40Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)
• Comparison of Machine Learning Classifiers to Predict Patient Survival and Genetics of GBM: Towards a Standardized Model for Clinical Implementation [44.02622933605018]
  Radiomic models have been shown to outperform clinical data for outcome prediction in glioblastoma (GBM) We aimed to compare nine machine learning classifiers to predict overall survival (OS), isocitrate dehydrogenase (IDH) mutation, O-6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation, epidermal growth factor receptor (EGFR) VII amplification and Ki-67 expression in GBM patients. xGB obtained maximum accuracy for OS (74.5%), AB for IDH mutation (88%), MGMT methylation (71,7%), Ki-67 expression (86,6%), and EGFR amplification (81,
  arXiv  Detail & Related papers  (2021-02-10T15:10:37Z)
• Self-Training with Improved Regularization for Sample-Efficient Chest X-Ray Classification [80.00316465793702]
  We present a deep learning framework that enables robust modeling in challenging scenarios. Our results show that using 85% lesser labeled data, we can build predictive models that match the performance of classifiers trained in a large-scale data setting.
  arXiv  Detail & Related papers  (2020-05-03T02:36:00Z)
• Introducing Anisotropic Minkowski Functionals for Local Structure Analysis and Prediction of Biomechanical Strength of Proximal Femur Specimens [0.0]
  Bone fragility and fracture caused by osteoporosis or injury are prevalent in adults over the age of 50 and can reduce their quality of life. This study proposes a new method to predict the bone strength of proximal femur specimens from quantitative multi-detector computer tomography (MDCT) images.
  arXiv  Detail & Related papers  (2020-04-02T14:33:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.