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BackgroundMethodResultsCardiovascular diseases (CVDs) include abnormal conditions of the heart, diseased blood vessels, structural problems of the heart, and blood clots. Traditionally, CVD has been diagnosed by clinical experts, physicians, and medical specialists, which is expensive, time-consuming, and requires expert intervention. On the other hand, cost-effective digital diagnosis of CVD is now possible because of the emergence of machine learning (ML) and statistical techniques.In this research, extensive studies were carried out to classify CVD via 19 promising ML models. To evaluate the performance and rank the ML models for CVD classification, two benchmark CVD datasets are considered from well-known sources, such as Kaggle and the UCI repository. The results are analysed considering individual datasets and their combination to assess the efficiency and reliability of ML models on the basis of various performance measures, such as precision, kappa, accuracy, recall, and the F1 score. Since some of the ML models are stochastic, we repeated the simulation 50 times for each dataset using each model and applied nonparametric statistical tests to draw decisive conclusions.The nonparametric Friedman – Nemenyi hypothesis test suggests that the Extra Tree Classifier provides statistically superior accuracy and precision compared with all other models. However, the Extreme Gradient Boost (XGBoost) classifier provides statistically superior recall, kappa, and F1 scores compared with those of all the other models. Additionally, the XGBRF classifier achieves a statistically second-best rank in terms of the recall measures. [ABSTRACT FROM AUTHOR]

Background: The liver is an internal organ located in the upper right section of the abdomen, just beneath the diaphragm, and near the stomach. It performs numerous functions that are essential for metabolism, digestion, detoxification, and nutrient storage. Several types of liver cancers are known, with the most common being hepatocellular carcinoma (HCC), which is the main type of liver cell called hepatocytes. Another less common type is cholangiocarcinoma, which originates in the bile ducts the liver. This study aimed to evaluate and compare machine-learning-based models for the early detection of liver cancer to improve diagnostic accuracy. Method: In this study, various models, such as SVM, decision tree, random forest, logistic regression, K-neighbor, Gaussian NB, AdaBoost classifier, MLP classifier, passive aggressive, ridge classifier, extra tree, bagging classifier, extra trees, gradient boosting, SGD classifier, linear SVC, voting classifier, and stacking classifier were used. Five performance metrics (accuracy, precision, recall, F1 score, and Cohen's kappa) were used to evaluate the performance of the proposed methods. Results: The dataset comprised 12 instances, and across all the models tested, we utilized the extra tree classifier for the early detection of liver cancer, achieving a notable accuracy of 85.8%. The model demonstrated a precision of 75.5%, while it achieved a high recall of 92.2%, and the F1 score of 83.2% underscored its robust performance, suggesting significant potential for enhancing diagnostic accuracy and necessitating further investigation. These performance metrics highlight the potential of the extra tree classifier to improve early detection strategies for liver cancer. Conclusion: After performing the complete process, we conclude that the extra tree classifier out of 17 models is the most suitable machine learning algorithm for liver cancer prediction. [ABSTRACT FROM AUTHOR]

Background This study was conducted to compare hippocampal volume in patients with TLE with that of a normal control group, and examine the degree and frequency of reductions in hippocampal volume in patients with prolonged epilepsy. Methods This was a hospital based cross-sectional observational study conducted among 228 patients who presented with epilepsy to the Department of Radio-Diagnosis at Ashwini Neuro-Trauma Center Cuttack, from 2015 to 2020 after obtaining clearance from the institutional ethics committee and written informed consent from the study participants. Results Hippocampal volume distribution in the left lobe as well as the right lobe was found to be significantly lower in epileptic subjects as compared to non-epileptic subjects. Using the Mann-Whitney U Test, the mean rank of left and right hippocampal volume comparisons among the group of subjects was found to be statistically significant. There was a significant difference in duration of seizure between epileptic and nonepileptic groups using the Mann-Whitney U test. It was seen that with an increase in seizure duration, there was a decrease in hippocampal volume, both left and right. The correlation was, however, poor but was found to be statistically significant for both left and right hippocampal volumes. Conclusion The ability of volumetric 3D imaging to predict the presence and laterality of hippocampal atrophy plays an integral role in the evaluation of patients with epilepsy, especially in patients with subtle findings and no perceptible hippocampal atrophy on visual inspection and, should be utilized on a regular basis in clinical practice as it is superior to visual analysis in detecting hippocampal atrophy, which can help in guiding surgical resections. [ABSTRACT FROM AUTHOR]