About half of the people who develop heart failure (HF) die within five years of diagnosis. Over the years, researchers have developed several machine learning-based models for the early prediction of HF and to help cardiologists to improve the diagnosis process. In this paper, we introduce an expert system that stacks two support vector machine (SVM) models for the effective prediction of HF. The first SVM model is linear and L 1 regularized. It has the capability to eliminate irrelevant features by shrinking their coefficients to zero. The second SVM model is L 2 regularized. It is used as a predictive model. To optimize the two models, we propose a hybrid grid search algorithm (HGSA) that is capable of optimizing the two models simultaneously. The effectiveness of the proposed method is evaluated using six different evaluation metrics: accuracy, sensitivity, specificity, the Matthews correlation coefficient (MCC), ROC charts, and area under the curve (AUC). The experimental results confirm that the proposed method improves the performance of a conventional SVM model by 3.3%. Moreover, the proposed method shows better performance compared to the ten previously proposed methods that achieved accuracies in the range of 57.85%-91.83%. In addition, the proposed method also shows better performance than the other state-of-the-art machine learning ensemble models.INDEX TERMS Clinical expert system, feature selection, heart failure prediction, hybrid grid search algorithm, support vector machine.
Heart failure is considered one of the leading cause of death around the world. The diagnosis of heart failure is a challenging task especially in underdeveloped and developing countries where there is a paucity of human experts and equipments. Hence, different researchers have developed different intelligent systems for automated detection of heart failure. However, most of these methods are facing the problem of overfitting i.e. the recently proposed methods improved heart failure detection accuracy on testing data while compromising heart failure detection accuracy on training data. Consequently, the constructed models overfit to the testing data. In order, to come up with an intelligent system that would show good performance on both training and testing data, in this paper we develop a novel diagnostic system. The proposed diagnostic system uses random search algorithm (RSA) for features selection and random forest model for heart failure prediction. The proposed diagnostic system is optimized using grid search algorithm. Two types of experiments are performed to evaluate the precision of the proposed method. In the first experiment, only random forest model is developed while in the second experiment the proposed RSA based random forest model is developed. Experiments are performed using an online heart failure database namely Cleveland dataset. The proposed method is efficient and less complex than conventional random forest model as it produces 3.3% higher accuracy than conventional random forest model while using only 7 features. Moreover, the proposed method shows better performance than five other state of the art machine learning models. In addition, the proposed method achieved classification accuracy of 93.33% while improving the training accuracy as well. Finally, the proposed method shows better performance than eleven recently proposed methods for heart failure detection.
Our investigation intended to analyze the chemical composition and the antioxidant activity of Carrichtera annua and to evaluate the antiproliferative effect of C. annua crude and phenolics extracts by MTT assay on a panel of cancerous and non-cancerous breast and liver cell lines. The total flavonoid and phenolic contents of C. annua were 47.3 ± 17.9 mg RE/g and 83.8 ± 5.3 mg respectively. C. annua extract exhibited remarkable antioxidant capacity (50.92 ± 5.64 mg GAE/g) in comparison with BHT (74.86 ± 3.92 mg GAE/g). Moreover, the extract exhibited promising reduction ability (1.17 mMol Fe+2/g) in comparison to the positive control (ascorbic acid with 2.75 ± 0.91) and it displayed some definite radical scavenging effect on DPPH (IC50 values of 211.9 ± 3.7 µg/mL). Chemical profiling of C. annua extract was achieved by LC-ESI-TOF-MS/MS analysis. Forty-nine hits mainly polyphenols were detected. Flavonoid fraction of C. annua was more active than the crude extract. It demonstrated selective cytotoxicity against the MCF-7 and HepG2 cells (IC50 = 13.04 and 19.3 µg/mL respectively), induced cell cycle arrest at pre-G1 and G2/M-phases and displayed apoptotic effect. Molecular docking studies supported our findings and revealed that kaempferol-3,7-O-bis-α-L-rhamnoside and kaempferol-3-rutinoside were the most active inhibitors of Bcl-2. Therefore, C. annua herb seems to be a promising candidate to further advance anticancer research. In extrapolation, the intake of C. annua phenolics might be adventitious for alleviating breast and liver malignancies and tumoral proliferation in humans.
The Red Sea specimen of the marine sponge Hyrtios erectus (order Dictyoceratida) was found to contain scalarane-type sesterterpenes. 12-O-deacetyl-12,19-di-epi-scalarin (14), a new scalarane sesterterpenoid, along with fourteen previously-reported scalarane-type sesterterpenes (1–13 and 15) have been isolated. The chemical structures of the isolated compounds were elucidated on the basis of detailed 1D and 2D NMR spectral data and mass spectroscopy, as well as by comparison with reported data. The anti-Helicobacter pylori, antitubercular and cytotoxic activities of all fifteen compounds were evaluated to reveal the potency of Compounds 1, 2, 3, 4, 6, 7 and 10. Amongst these, Compounds 1, 3, 4, 6 and 10 displayed a promising bioactivity profile, possessing potent activities in the antitubercular and anti-H. pylori bioassay. Compounds 2 and 7 showed the most promising cytotoxic profile, while Compounds 1 and 10 showed a moderate cytotoxic profile against MCF-7, HCT-116 and HepG2 cell lines.
Heart failure (HF) is considered a deadliest disease worldwide. Therefore, different intelligent medical decision support systems have been widely proposed for detection of HF in literature. However, low rate of accuracies achieved on the HF data is a major problem in these decision support systems. To improve the prediction accuracy, we have developed a feature-driven decision support system consisting of two main stages. In the first stage, χ2 statistical model is used to rank the commonly used 13 HF features. Based on the χ2 test score, an optimal subset of features is searched using forward best-first search strategy. In the second stage, Gaussian Naive Bayes (GNB) classifier is used as a predictive model. The performance of the newly proposed method (χ2-GNB) is evaluated by using an online heart disease database of 297 subjects. Experimental results show that our proposed method could achieve a prediction accuracy of 93.33%. The developed method (i.e., χ2-GNB) improves the HF prediction performance of GNB model by 3.33%. Moreover, the newly proposed method also shows better performance than the available methods in literature that achieved accuracies in the range of 57.85–92.22%.
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