Efficient Selection of Gaussian Kernel SVM Parameters for Imbalanced Data.

For medical data mining, the development of a class prediction model has been widely used to deal with various kinds of data classification problems. Classification models especially for high-dimensional gene expression datasets have attracted many researchers in order to identify marker genes for distinguishing any type of cancer cells from their corresponding normal cells. However, skewed class distributions often occur in the medical datasets in which at least one of the classes has a relatively small number of observations. A classifier induced by such an imbalanced dataset typically has a high accuracy for the majority class and poor prediction for the minority class. In this study, we focus on an SVM classifier with a Gaussian radial basis kernel for a binary classification problem. In order to take advantage of an SVM and to achieve the best generalization ability for improving the classification performance, we will address two important problems: the class imbalance and parameter selection during SVM parameter optimization. First of all, we proposed a novel adjustment method called b-SVM, for adjusting the cutoff threshold of the SVM. Second, we proposed a fast and simple approach, called the Min-max gamma selection, to optimize the model parameters of SVMs without carrying out an extensive k-fold cross validation. An extensive comparison with a standard SVM and well-known existing methods are carried out to evaluate the performance of our proposed algorithms using simulated and real datasets. The experimental results show that our proposed algorithms outperform the over-sampling techniques and existing SVM-based solutions. This study also shows that the proposed Min-max gamma selection is at least 10 times faster than the cross-validation selection based on the average running time on six real datasets.

Assessing the causal relationships between gout and hypertension: a bidirectional Mendelian randomisation study with coarsened exposures.

OBJECTIVES: Observational studies have demonstrated associations between gout and hypertension, but whether they are causal remains unclear. Our work aims to assess the causal relationship between gout and hypertension. METHODS: We obtained genetic information from the Taiwan Biobank, including 88,347 participants and 686,439 single-nucleotide polymorphisms (SNPs). A novel model of Mendelian randomisation (MR) with coarsened exposures was used to examine the causality between the liability of gout on hypertension and vice versa, using 4 SNPs associated with gout and 10 SNPs associated with hypertension after removal of SNPs associated with measured confounders. The binary exposure (gout/hypertension) can be considered a coarsened approximation of a latent continuous trait. The inverse-variance weighted (IVW) and polygenic risk score (PRS) methods were used to estimate effect size. The MR analysis with coarsened exposures was performed with and without adjustments for covariates. RESULTS: Of the 88,347 participants, 3253 (3.68%) had gout and 11,948 (13.52%) had hypertension (men, 31.9%; mean age 51.1 [SD, 11.1] years). After adjusting to measured confounders, MR analysis with coarsened exposures showed a significant positive causal effect of the liability of gout on hypertension in both the IVW method (relative risk [RR], 1.10; 95% confidence interval [CI], 1.03-1.19; p = 0.0077) and the PRS method (RR, 1.10; 95% CI, 1.02-1.19; p = 0.0092). The result of causality was the same before and after involving measured confounders. However, there was no causal effect of the liability of hypertension on gout. CONCLUSIONS: In this study, we showed that the liability of gout has a causal effect on hypertension, but the liability of hypertension does not have a causal effect on gout. Adequate management of gout may reduce the risk of developing hypertension.

Factors predicting improved compliance towards colonoscopy in individuals with positive faecal immunochemical test (FIT).

OBJECTIVES: Follow-up colonoscopy after a positive faecal immunochemical test (FIT) in any colorectal cancer (CRC) screening programme is integral. However, many individuals who had a positive FIT declined colonoscopy subsequently. This study aims to uncover the predictors on completion of colonoscopy using the Health Belief Model (HBM) between individuals who complete and those who did not after a positive FIT. METHODS: A mixed-method study comprising qualitative semi-structured interviews followed by a locally validated questionnaire in Singapore was prospectively administered via telephone interview to average risk individuals with positive FIT results from a cohort of the national FIT screening database referred for follow-up colonoscopic evaluation. RESULTS: A total of 394 individuals, with a median age of 66 years (range, 46-89 years), were recruited. Fifty percent completed follow-up colonoscopic evaluation and formed the "doers" group. All participants demonstrated high knowledge of symptoms of CRC and awareness and qualitative responses were aligned to the various HBM domains. Using multi-variable analysis, doers felt that medical recommendations (odds ratio [OR], 2.39, 95% confidence interval [CI]: 1.23-4.63, p = 0.01) and mainstream media publicity (OR, 2.16, 95% CI: 1.09-4.26, p = 0.026) were important. Non-doers showed positive association with perceived barriers such as cost (OR, 2.15, 95% CI: 1.10-4.20, p = 0.026) and inconvenience (OR, 3.44, 95% CI: 1.50-7.89, p = 0.004). CONCLUSIONS: Identified factors such as tackling perceived barriers, public health education and active promotion by medical physicians, family and friends could help guide subsequent interventions to improve compliance of individuals with positive FIT to undergo follow-up colonoscopy.