Classification Techniques for Cardio-Vascular Diseases Using Supervised Machine Learning.

INTRODUCTION: The World Health Organization has estimated that 12 million deaths occur worldwide, every year due to Heart diseases. Half the deaths in the developed countries are due to cardiovascular diseases. The early prognosis of cardiovascular diseases can aid in making decisions on lifestyle changes in high risk patients. AIM: The aim of this paper is to build and compare classification techniques for cardiovascular diseases. METHODS: The dataset contained 4270 patients and 14 attributes and it is available on the UCI data repository. The prediction is a binary outcome (event and no event). Variables of each attribute is a potential risk factor. There are both demographic, behavioral and medical risk factors. The classification goal is to predict whether the patient has 10-year risk of future coronary heart disease (CHD). RESULTS: Different classifiers were tested. The SMOTE technique was used in order to solve the class imbalance. The cross-validation method was used in order to estimate how accurately our predictive models will perform. We evaluate our classifiers by using the following metrics: precision, recall, F1-score, Accuracy, AUC (Area Under Curve). CONCLUSIONS: Based on the resluts, the best scores have the Random Forest and Decision Tree classifiers.

Health Professionals' Perception about Big Data Technology in Greece.

INTRODUCTION: Big data is massive amounts of information that can work wonders. In the healthcare industry, various sources for big data include hospital records, medical records of patients, results of medical examinations, and devices that are a part of internet of things. AIM: The research aim of this study is to investigate the perceptions of the Health Professionals about the Big Data Technology in Healthcare. METHODS: An empirical study was conducted among 151 health professionals (doctors and nurses) to assess their knowledge about the Big Data Technology and their perceptions about using this technology in healthcare. A questionnaire was developed in order to measure the aforementioned dimensions. RESULTS: The survey's population was formed by 151 doctors and nurses who are working at private and public hospitals in Greece. The majority of the population have never heard about Big Data. As a result, most of them were not aware of the format of Big Data. CONCLUSION: Based on the study findings, it can be assumed that the majority of the responders did not have knowledge about the Big Data Technology. It is also important that most of them had never been informed about Big Data. It can be assumed that the Healthcare Sector in Greece is not familiar with Big Data Technology yet. Finally,the current study reveals a rather positive attitude toward the usage of Big Data in the Helathcare domain, although there are some doubts about the implementation of the aforementioned technology in the Greek national healthcare system.

Health Informatics Scientists' Perception About Big Data Technology.

The aim of this paper is to present the perceptions of the Health Informatics Scientists about the Big Data Technology in Healthcare. An empirical study was conducted among 46 scientists to assess their knowledge about the Big Data Technology and their perceptions about using this technology in healthcare. Based on the study findings, 86.7% of the scientists had knowledge of Big data Technology. Furthermore, 59.1% of the scientists believed that Big Data Technology refers to structured data. Additionally, 100% of the population believed that Big Data Technology can be implemented in Healthcare. Finally, the majority does not know any cases of use of Big Data Technology in Greece while 57,8% of the them mentioned that they knew use cases of the Big Data Technology abroad.

Investigating the Perceived Innovation of the Big Data Technology in Healthcare.

The research aim of this study is to investigate the perceived innovation of the Big Data Technology in Healthcare. A survey was conducted using a theoretical model based on Rogers' Innovation Diffusion Theory, on Davis' Technology Acceptance Model, and relative research work. The results reveal that the Big Data Technology may be an innovation on the field of Health Informatics as it offers a lot of advantages compared to the traditional ways of data handling and processing, and it is compatible with the current technological status on the healthcare domain. Additionally, the current study presents the positive attitude of the Informatics Experts about the usage of the Big Data innovative technology on Health sector.