Predicting postoperative complications for gastric cancer patients using data mining

Gastric cancer refers to the development of malign cells that can grow in any part of the stomach. With the vast amount of data being collected daily in healthcare environments, it is possible to develop new algorithms which can support the decision-making processes in gastric cancer patients treatment. This paper aims to predict, using the CRISP-DM methodology, the outcome from the hospitalization of gastric cancer patients who have undergone surgery, as well as the occurrence of postoperative complications during surgery. The study showed that, on one hand, the RF and NB algorithms are the best in the detection of an outcome of hospitalization, taking into account patients’ clinical data. On the other hand, the algorithms J48, RF, and NB offer better results in predicting postoperative complications.FCT - Fundação para a Ciência e a Tecnologia (UID/CEC/00319/2013

propnet: Propagating 2D Annotation to 3D Segmentation for Gastric Tumors on CT Scans

**Background:** Accurate 3D CT scan segmentation of gastric tumors is pivotal for diagnosis and treatment. The challenges lie in the irregular shapes, blurred boundaries of tumors, and the inefficiency of existing methods. **Purpose:** We conducted a study to introduce a model, utilizing human-guided knowledge and unique modules, to address the challenges of 3D tumor segmentation. **Methods:** We developed the PropNet framework, propagating radiologists' knowledge from 2D annotations to the entire 3D space. This model consists of a proposing stage for coarse segmentation and a refining stage for improved segmentation, using two-way branches for enhanced performance and an up-down strategy for efficiency. **Results:** With 98 patient scans for training and 30 for validation, our method achieves a significant agreement with manual annotation (Dice of 0.803) and improves efficiency. The performance is comparable in different scenarios and with various radiologists' annotations (Dice between 0.785 and 0.803). Moreover, the model shows improved prognostic prediction performance (C-index of 0.620 vs. 0.576) on an independent validation set of 42 patients with advanced gastric cancer. **Conclusions:** Our model generates accurate tumor segmentation efficiently and stably, improving prognostic performance and reducing high-throughput image reading workload. This model can accelerate the quantitative analysis of gastric tumors and enhance downstream task performance

Large-scale survey to estimate the prevalence of disorders for 192 Kennel Club registered breeds

Abstract Background Pedigree or purebred dogs are often stated to have high prevalence of disorders which are commonly assumed to be a consequence of inbreeding and selection for exaggerated features. However, few studies empirically report and rank the prevalence of disorders across breeds although such data are of critical importance in the prioritisation of multiple health concerns, and to provide a baseline against which to explore changes over time. This paper reports an owner survey that gathered disorder information on Kennel Club registered pedigree dogs, regardless of whether these disorders received veterinary care. This study aimed to determine the prevalence of disorders among pedigree dogs overall and, where possible, determine any variation among breeds. Results This study included morbidity data on 43,005 live dogs registered with the Kennel Club. Just under two thirds of live dogs had no reported diseases/conditions. The most prevalent diseases/conditions overall were lipoma (4.3%; 95% confidence interval 4.13-4.52%), skin (cutaneous) cyst (3.1%; 2.94-3.27%) and hypersensitivity (allergic) skin disorder (2.7%; 2.52-2.82%). For the most common disorders in the most represented breeds, 90 significant differences between the within breed prevalence and the overall prevalence are reported. Conclusion The results from this study have added vital epidemiological data on disorders in UK dogs. It is anticipated that these results will contribute to the forthcoming Breed Health & Conservation Plans, a Kennel Club initiative aiming to assist in the identification and prioritisation of breeding selection objectives for health and provide advice to breeders/owners regarding steps that may be taken to minimise the risk of the disease/disorders. Future breed-specific studies are recommended to report more precise prevalence estimates within more breeds

Data Mining Algorithms Predicting Different Types of Cancer: Integrative Literature Review

Based on the World Health Organization, cancer is the second leading cause of death globally and is responsible for an estimated 9.6 million deaths in 2018. Globally, about 1 in 6 deaths is due to cancer, and approximately 70% of deaths from cancer occur in low and middle-income countries. with accelerating developments in technologies and the digitization of healthcare, a lot of cancer\u27s data have been collected, and multiple cancer repositories have been created as a result. cancer has become a data-intensive area of research over the last decade. A large number of researchers have used data mining algorithms in predicting different types of cancer to reduce the cost of tests used to predict different types of cancer, especially in low and middle-income countries. This paper reports on a systematic examination of the literature on data mining algorithms predicting different types of cancer through which we provide a thorough review, analysis, and synthesis of research published in the past 10 years. We follow the systematic literature review methodology to examine theories, problems, methodologies, and major findings of related studies on data mining algorithms predicting cancer that were published between 2009 and 2019. Using thematic analysis, we develop a research taxonomy that summarizes the main algorithms used in the existing research in the field, and we identify the most used data mining algorithms in predicting different types of cancer. In addition, to data mining algorithms used in predicting each type of cancer, as mentioned in the reviewed studies. We also identify the most popular types of cancer that researchers tackled using predictive analytics

AI in Oncology - Precision Therapy & Prognosis

Artificial intelligence (AI) has strong logical reasoning abilities and the ability to learn on its own, and it can mimic the human brain's thought process. Machine learning and other AI technologies have the potential to greatly enhance the existing method of anticancer medicine development. However, AI currently has several limits. This study investigates the evolution of artificial intelligence technologies in anti-cancer therapeutic research, such as deep learning and machine learning. At the same time, we are optimistic about AI's future

Common human diseases prediction using machine learning based on survey data

In this era, the moment has arrived to move away from disease as the primary emphasis of medical treatment. Although impressive, the multiple techniques that have been developed to detect the diseases. In this time, there are some types of diseases COVID-19, normal flue, migraine, lung disease, heart disease, kidney disease, diabetics, stomach disease, gastric, bone disease, autism are the very common diseases. In this analysis, we analyze disease symptoms and have done disease predictions based on their symptoms. We studied a range of symptoms and took a survey from people in order to complete the task. Several classification algorithms have been employed to train the model. Furthermore, performance evaluation matrices are used to measure the model's performance. Finally, we discovered that the part classifier surpasses the others.Comment: 11 pages, 6 figures, accepted in Bulletin of Electrical Engineering and Informatics Journa