Colorectal cancer is the leading cause of cancer-related deaths worldwide, and early detection has proven to be an effective method for reducing mortality. The machine learning method can be implemented to build a noninvasive stratifying tool that helps identify patients with potential colorectal precancerous lesions (polyps). This study aimed to develop a noninvasive risk-stratified tool for colorectal polyps in asymptomatic, healthy participants. A total of 20,129 consecutive asymptomatic patients who underwent a health checkup between January 2005 and August 2007 were recruited. Positive relationships between noninvasive risk factors, such as age, Helicobacter pylori infection, hypertension, gallbladder polyps/stone, and BMI and colorectal polyps were observed (p < 0.0001), regardless of sex, whereas significant findings were noted in men with tooth disease (p = 0.0053). A risk stratification tool was developed, for colorectal polyps, that considers annual checkup results from noninvasive examinations. For the noninvasive stratified tool, the area under the receiver operating characteristic curve (AUC) of obese females (males) aged 50 years old), the AUCs of the stratifying tools were >85%. Our results indicate that the risk stratification tool can be built by using random forest and serve as an efficient noninvasive tool to identify patients requiring colonoscopy.

Early detection of lung cancer has a higher likelihood of curative treatment and thus improves survival rate. Low-dose computed tomography (LDCT) screening has been shown to be effective for high-risk individuals in several clinical trials, but has high false positive rates. To evaluate the risk of stage I lung cancer in the general population not limited to smokers, a retrospective study of 133 subjects was conducted in a medical center in Taiwan. Regularized regression was used to build the risk prediction model by using LDCT and health examinations. The proposed model selected seven variables related to nodule morphology, counts and location, and ten variables related to blood tests and medical history, achieving an area under the curve (AUC) value of 0.93. The higher the age, white blood cell count (WBC), blood urea nitrogen (BUN), diabetes, gout, chronic obstructive pulmonary disease (COPD), other cancers, and the presence of spiculation, ground-glass opacity (GGO), and part solid nodules, the higher the risk of lung cancer. Subjects with calcification, solid nodules, nodules in the middle lobes, more nodules, and diseases related to thyroid, liver, and digestive systems were at a lower risk. The selected variables did not indicate causation.

Evaluating and comparing process capabilities are important tasks of production management. Manufacturers should apply the process with the highest capability among competing processes. A process group selection method is developed to solve the process selection problem based on overall yields. The goal is to select the processes with the highest overall yield among I processes under multiple quality characteristics, I > 2. The proposed method uses Bonferroni adjustment to control the overall error rate of comparing multiple processes. The critical values and the required sample sizes for designated powers are provided for practical use.