Automated Brain Tumor Diagnosis Using Deep Residual U-Net Segmentation燤odel

Bhat

Computational and Mathematical Methods in Medicine

et al. 2023

Self Cite

The leading cause of death worldwide today is heart disease (HD). The heart is recognised as the second-most significant organ behind the brain. A successful outcome of treatment can be improved by an early diagnosis which can significantly reduce the chance of death in health care. In this paper, we proposed a method to predict heart disease. We used various machine learning algorithms (MLA), namely, logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), Naive Bayes (NB), random forest (RF), and decision tree (DT). With the testing data set, we evaluated the model’s accuracy in heart disease prediction. When compared to the other five models, the random forest and k-nearest neighbor approaches perform better. With a 99.04% accuracy rate, the k-nearest neighbor algorithm and random forest provide the best match to the data as compared to other algorithms. Six feature selection algorithms were used for the performance evaluation matrix. MCC parameters for accuracy, precision, recall, and F measure are used to evaluate models.

Section: Related Workmentioning

confidence: 99%

Performance Evaluation of Machine Learning Techniques (MLT) for Heart Disease Prediction

Bhat

Computational and Mathematical Methods in Medicine

et al. 2023

Self Cite

“…score on at least a one-fold basis on the cup-segmented fundus dataset. All methods perform well on the boundarydamaged fundus data set, given their median scores and distribution [50].…”

Section: Level Set Algorithm For Optic Cup Localisationmentioning

confidence: 99%

Artificial Intelligence Approaches in Healthcare Informatics Toward Advanced Computation and Analysis

Priyanka,

Thangavel,

Mohanasundaram

et al. 2024

TOBEJ

Introduction Automated Machine Learning or AutoML is a set of approaches and processes to make machine learning accessible for non-experts. AutoML can exhibit optimized enhancement of an existing model or suggest the best models for precise datasets. In the field of computerized Artificial Intelligence (AI), medical experts better utilize AI models with available encrypted information science ability. Methods This paper aims to characterize and summarize the stage-wise design of Automated Machine Learning (AutoML) analysis e-healthcare platform starting from the sensing layer and transmission to the cloud using IoT (Internet of Things). To support the AutoML concept, the Auto Weka2.0 package, which serves as the open-source software platform, holds the predominant priority for experimental analysis to generate statistical reports. Results To validate the entire framework, a case study on Glaucoma diagnosis using the AutoML concept is carried out, and its identification of best-fit model configuration rates is also presented. The Auto-ML built-in model possesses a higher influence factor to generate population-level statistics from the available individual patient histories. Conclusion Further, AutoML is integrated with the Closed-loop Healthcare Feature Store (CHFS) to support data analysts with an automated end-to-end ML pipeline to help clinical experts provide better medical examination through automated mode.

Computational and Mathematical Methods in Medicine

“…Ahmad et al [ 25 ] employed radix tree structure to decrease the computational difficulty in deep learning. Poonguzhali et al [ 26 ] proposed an automated deep residual U-Net segmentation method for medicinal image database. Some researches have focused on the interpretability of deep learning model, especially for medical data.…”

Section: Related Workmentioning

confidence: 99%

Multiview Deep Forest for Overall Survival Prediction in Cancer

Chen

et al. 2023

Overall survival (OS) in cancer is crucial for cancer treatment. Many machine learning methods have been applied to predict OS, but there are still the challenges of dealing with multiview data and overfitting. To overcome these problems, we propose a multiview deep forest (MVDF) in this paper. MVDF can learn the features of each view and fuse them with integrated learning and multiple kernel learning. Then, a gradient boost forest based on the information bottleneck theory is proposed to reduce redundant information and avoid overfitting. In addition, a pruning strategy for a cascaded forest is used to limit the impact of outlier data. Comprehensive experiments have been carried out on a data set from West China Hospital of Sichuan University and two public data sets. Results have demonstrated that our method outperforms the compared methods in predicting overall survival.