A hybrid feature selection algorithm combining information gain and grouping particle swarm optimization for cancer diagnosis.

BACKGROUND: Cancer diagnosis based on machine learning has become a popular application direction. Support vector machine (SVM), as a classical machine learning algorithm, has been widely used in cancer diagnosis because of its advantages in high-dimensional and small sample data. However, due to the high-dimensional feature space and high feature redundancy of gene expression data, SVM faces the problem of poor classification effect when dealing with such data. METHODS: Based on this, this paper proposes a hybrid feature selection algorithm combining information gain and grouping particle swarm optimization (IG-GPSO). The algorithm firstly calculates the information gain values of the features and ranks them in descending order according to the value. Then, ranked features are grouped according to the information index, so that the features in the group are close, and the features outside the group are sparse. Finally, grouped features are searched using grouping PSO and evaluated according to in-group and out-group. RESULTS: Experimental results show that the average accuracy (ACC) of the SVM on the feature subset selected by the IG-GPSO is 98.50%, which is significantly better than the traditional feature selection algorithm. Compared with KNN, the classification effect of the feature subset selected by the IG-GPSO is still optimal. In addition, the results of multiple comparison tests show that the feature selection effect of the IG-GPSO is significantly better than that of traditional feature selection algorithms. CONCLUSION: The feature subset selected by IG-GPSO not only has the best classification effect, but also has the least feature scale (FS). More importantly, the IG-GPSO significantly improves the ACC of SVM in cancer diagnostic.

A hybrid sampling algorithm combining synthetic minority over-sampling technique and edited nearest neighbor for missed abortion diagnosis.

BACKGROUND: Clinical diagnosis based on machine learning usually uses case samples as training samples, and uses machine learning to construct disease prediction models characterized by descriptive texts of clinical manifestations. However, the problem of sample imbalance often exists in the medical field, which leads to a decrease in classification performance of the machine learning. METHODS: To solve the problem of sample imbalance in medical dataset, we propose a hybrid sampling algorithm combining synthetic minority over-sampling technique (SMOTE) and edited nearest neighbor (ENN). Firstly, the SMOTE is used to over-sampling missed abortion and diabetes datasets, so that the number of samples of the two classes is balanced. Then, ENN is used to under-sampling the over-sampled dataset to delete the "noisy sample" in the majority. Finally, Random forest is used to model and predict the sampled missed abortion and diabetes datasets to achieve an accurate clinical diagnosis. RESULTS: Experimental results show that Random forest has the best classification performance on missed abortion and diabetes datasets after SMOTE-ENN sampled, and the MCC index is 95.6% and 90.0%, respectively. In addition, the results of pairwise comparison and multiple comparisons show that the SMOTE-ENN is significantly better than other sampling algorithms. CONCLUSION: Random forest has significantly improved all indexes on the missed abortion dataset after SMOTE-ENN sampled.

Loading of Zn/ZnO particles in the precursor feedstock affects the characteristics of liquid plasma sprayed nano-ZnO coatings for photocatalytic applications.

It is known that ZnO is an n-type semiconductor with photocatalytic performances under ultraviolet light irradiation. Constructing a superior structure for a modified electron band has been one of the major research goals for photocatalytic ZnO. Here we report a new technical route for making nano-ZnO coatings with a porous topographic morphology. The coatings were fabricated by plasma spraying the mixture of suspension and solution liquid precursors. Pre-loading of ZnO and Zn powders in the precursor was carried out for the purpose of tailoring the structure of the coatings. The coatings in micron thicknesses showed a porous skeleton and a fluffy top layer consisting of ultrafine ZnO grains. Photocatalytic testing by measuring the degradation of methylene blue revealed significantly enhanced activities of the coatings deposited using the ZnO/Zn loaded precursor. The hybrid-structured ZnO coatings exhibited a narrowed band gap and modified oxygen defects as compared to those deposited from the single liquid feedstock. The results shed light on a one-step easy thermal spray fabrication of polytropic nanostructured functional coatings by employing solid powder-loaded liquid as the starting feedstock.