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The Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers (both academic and industrial) through all stages of data analysis, model building and implementation.

\"Data science is increasingly important in the business world, not just for the teams in charge of implementing it but the professionals adjacent to them. Yet not all businesspeople have a general understanding of the basics-and if senior management assigns them to work alongside a data science team, they'll need that knowledge as soon as possible without having to take online courses or dive down the Internet rabbit hole. This book provides that knowledge base, walking readers through the key ideas needed to communicate and work with a data science team. They will be able to understand the basic technical lingo, recognize the types of talent on the team and pose good questions to your data scientists to open up more insights, create opportunities, and generate value. By the end of the book they will be able to answer key questions including how data is collected and stored, what hardware and software tools are needed to analyze data, who does what on the data science team and which models should be considered for specific projects. Most critically, they will also be armed with critical questions that you can use to further probe data analysts, statisticians, data scientists and other technical experts to better understand the value of their work for a business\"-- Provided by publisher.

No classification methods to predict prognosis after carbon‐ion radiotherapy for hepatocellular carcinoma have yet been reported. This study aimed to develop risk classification for cancer‐specific survival (CSS) after carbon‐ion radiotherapy for hepatocellular carcinoma using decision tree analysis as a data‐mining method. In this single‐center, retrospective study, we analyzed 90 consecutive patients with hepatocellular carcinoma treated with carbon‐ion radiotherapy between 2018 and 2022. Liver tumors were irradiated at 60 Gy (relative biological effectiveness [RBE]) in four fractions. If the tumor was close to the gastrointestinal tract, it was irradiated at 60 Gy [RBE] in 12 fractions. Univariate and multivariate analyses of progression‐free survival (PFS) and CSS were performed to assess patients' background and treatment‐related factors. Decision tree analysis (DTA) was performed to assess prognostic factors for CSS that were significantly different in the multivariate analysis. The median follow‐up period was 32.8 months for all patients and 35.6 months for survivors. Multivariate analysis identified dose fractionation and pretreatment alpha‐fetoprotein values as significant prognostic factors for PFS and CSS. Moreover, clinical stage and pretreatment protein induced by vitamin K absence or antagonist ΙΙ values were significant prognostic factors for CSS. DTA revealed that the patients could be divided into three groups according to prognosis: low‐risk, high‐risk, and intermediate‐risk. Consequently, the 3‐year CSS rates for the low‐, intermediate‐, and high‐risk groups were 100%, 73.3%, and 44.4%, respectively. DTA represents a new method for risk classification for CSS after carbon‐ion radiotherapy for hepatocellular carcinoma based on tumor markers and clinical stage. First study to use decision tree analysis for predicting cancer‐specific survival after carbon‐ion radiotherapy for hepatocellular carcinoma. Decision tree model offers clear, patient‐friendly prognosis visualization.

Master advanced topics in the analysis of large, dynamically dependent datasets with this insightful resource Statistical Learning with Big Dependent Data delivers a comprehensive presentation of the statistical and machine learning methods useful for analyzing and forecasting large and dynamically dependent data sets.

Mental disorders have become a major concern in public health, and they are one of the main causes of the overall disease burden worldwide. Social media platforms allow us to observe the activities, thoughts, and feelings of people's daily lives, including those of patients suffering from mental disorders. There are studies that have analyzed the influence of mental disorders, including depression, in the behavior of social media users, but they have been usually focused on messages written in English. The study aimed to identify the linguistic features of tweets in Spanish and the behavioral patterns of Twitter users who generate them, which could suggest signs of depression. This study was developed in 2 steps. In the first step, the selection of users and the compilation of tweets were performed. A total of 3 datasets of tweets were created, a depressive users dataset (made up of the timeline of 90 users who explicitly mentioned that they suffer from depression), a depressive tweets dataset (a manual selection of tweets from the previous users, which included expressions indicative of depression), and a control dataset (made up of the timeline of 450 randomly selected users). In the second step, the comparison and analysis of the 3 datasets of tweets were carried out. In comparison with the control dataset, the depressive users are less active in posting tweets, doing it more frequently between 23:00 and 6:00 (P<.001). The percentage of nouns used by the control dataset almost doubles that of the depressive users (P<.001). By contrast, the use of verbs is more common in the depressive users dataset (P<.001). The first-person singular pronoun was by far the most used in the depressive users dataset (80%), and the first- and the second-person plural pronouns were the least frequent (0.4% in both cases), this distribution being different from that of the control dataset (P<.001). Emotions related to sadness, anger, and disgust were more common in the depressive users and depressive tweets datasets, with significant differences when comparing these datasets with the control dataset (P<.001). As for negation words, they were detected in 34% and 46% of tweets in among depressive users and in depressive tweets, respectively, which are significantly different from the control dataset (P<.001). Negative polarity was more frequent in the depressive users (54%) and depressive tweets (65%) datasets than in the control dataset (43.5%; P<.001). Twitter users who are potentially suffering from depression modify the general characteristics of their language and the way they interact on social media. On the basis of these changes, these users can be monitored and supported, thus introducing new opportunities for studying depression and providing additional health care services to people with this disorder.

Background Machine learning tools identify patients with blood counts indicating greater likelihood of colorectal cancer and warranting colonoscopy referral. Aims To validate a machine learning colorectal cancer detection model on a US community-based insured adult population. Methods Eligible colorectal cancer cases (439 females, 461 males) with complete blood counts before diagnosis were identified from Kaiser Permanente Northwest Region’s Tumor Registry. Control patients ( n  = 9108) were randomly selected from KPNW’s population who had no cancers, received at ≥1 blood count, had continuous enrollment from 180 days prior to the blood count through 24 months after the count, and were aged 40–89. For each control, one blood count was randomly selected as the pseudo-colorectal cancer diagnosis date for matching to cases, and assigned a “calendar year” based on the count date. For each calendar year, 18 controls were randomly selected to match the general enrollment’s 10-year age groups and lengths of continuous enrollment. Prediction performance was evaluated by area under the curve, specificity, and odds ratios. Results Area under the receiver operating characteristics curve for detecting colorectal cancer was 0.80 ± 0.01. At 99% specificity, the odds ratio for association of a high-risk detection score with colorectal cancer was 34.7 (95% CI 28.9–40.4). The detection model had the highest accuracy in identifying right-sided colorectal cancers. Conclusions ColonFlag ® identifies individuals with tenfold higher risk of undiagnosed colorectal cancer at curable stages (0/I/II), flags colorectal tumors 180–360 days prior to usual clinical diagnosis, and is more accurate at identifying right-sided (compared to left-sided) colorectal cancers.