Machine Learning and Artificial Neural Network for Predicting Heart Failure Risk

Heart failure is now widely spread throughout the world. Heart disease affects approximately 48% of the population. It is too expensive and also difficult to cure the disease. This research paper represents machine learning models to predict heart failure. The fundamental concept is to compare the correctness of various Machine Learning (ML) algorithms and boost algorithms to improve models’ accuracy for prediction. Some supervised algorithms like K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF), Logistic Regression (LR) are considered to achieve the best results. Some boosting algorithms like Extreme Gradient Boosting (XGBoost) and CatBoost are also used to improve the prediction using Artificial Neural Networks (ANN). This research also focuses on data visualization to identify patterns, trends, and outliers in a massive data set. Python and Scikit-learns are used for ML. Tensor Flow and Keras, along with Python, are used for ANN model training. The DT and RF algorithms achieved the highest accuracy of 95% among the classifiers. Meanwhile, KNN obtained a second height accuracy of 93.33%. XGBoost had a gratified accuracy of 91.67%, SVM, CATBoost, and ANN had an accuracy of 90%, and LR had 88.33% accuracy.     

一种模型决策森林算法

随机森林(RF)具有抗噪能力强,预测准确率高,能够处理高维数据等优点,因此在机器学习领域得到了广泛的应用。模型决策树(MDT)是一种加速的决策树算法,虽然能够提高决策树算法的训练效率,但是随着非纯伪叶结点规模的增大,模型决策树的精度也在下降。针对上述问题,提出了一种模型决策森林算法(MDF)以提高模型决策树的分类精度。MDF算法将MDT作为基分类器,利用随机森林的思想,生成多棵模型决策树。算法首先通过旋转矩阵得到不同的样本子集,然后在这些样本子集上训练出多棵不同的模型决策树,再将这些树通过投票的方式进行集成,最后根据得到的模型决策森林给出分类结果。在标准数据集上的实验结果表明,提出的模型决策森林在分类精度上明显优于模型决策树算法,并且MDF在树的数量较少时也能取到不错的精度,避免了因树的数量增加时间复杂度增高的问题。     

基于GA的仪表新产品功能结构方案推理机模型关键技术研究

虚拟原型逼真设计是近几年发展起来的新技术,主要研究虚拟环境下的产品建模问题以及基于仿真的集成产品和过程设计问题．产品功能结构方案推理机模型是虚拟原型逼真设计实现的关键技术之一．针对功能模块组合方案优化问题的复杂性,研究了基于遗传算法（GA）的功能结构方案推理机模型的实现技术,提出了双链异或杂交算子和自适应调节杂交概率和突变算子选择等算法,结合仪表产品设计应用实例,验证了模型的优越性,并将其应用于仪表新产品开发决策支持系统——仪表LC—QDPDS软件系统中．     

Molecular modeling study on the effect of residues distant from the nucleotide-binding portion on RNA binding in Staphylococcus aureus Hfq

Hfq is an abundant RNA-binding bacterial protein that was first identified in E. coli as a required host factor for phage Qβ RNA replication. The pleiotrophic phenotype resulting from the deletion of Hfq predicates the importance of this protein. Two RNA-binding sites have been characterized: the proximal site which binds sRNA and mRNA and the distal site which binds poly(A) tails. Previous studies mainly focused on the key residues in the proximal site of the protein. A recent mutation study in E. coli Hfq showed that a distal residue Val43 is important for the protein function. Interestingly, when we analyzed the sequence and structure of Staphylococcus aureus Hfq using the CONSEQ server, the results elicited that more functional residues were located far from the nucleotide-binding portion (NBP). From the analysis seven individual residues Asp9, Leu12, Glu13, Lys16, Gln31, Gly34 and Asp40 were selected to investigate the conformational changes in Hfq–RNA complex due to point mutation effect of those residues using molecular dynamics simulations. Results showed a significant effect on Asn28 which is an already known highly conserved functionally important residue. Mutants D9A, E13A and K16A depicted effects on base stacking along with increase in RNA pore diameter, which is required for the threading of RNA through the pore for the post-translational modification. Further, the result of protein stability analysis by the CUPSAT server showed destabilizing effect in the most mutants. From this study we characterized a series of important residues located far from the NBP and provide some clues that those residues may affect sRNA binding in Hfq.