Background/Purpose: In 1891, Gerard Philips and his father Frederik created the Dutch multinational corporation Philips in Eindhoven. Its headquarters are in Amsterdam. Having divesting off its consumer electronics division, Philips is now focused completely on the health technology industry. The company has extensive experience in a wide range of healthcare-related fields, including cardiology, health technology, oncology, respiratory medicine, fertility and pregnancy. To make people's lives better through innovation, and to contribute to the creation of a world that is both more sustainable and healthier. Objective: This paper provides a case study of Philips' transformation from an electronics firm to a leading healthcare product producer. This paper also looks at the healthcare business as a whole, as well as the many technological advancement components of it. Design/Methodology/Approach: Secondary sources were used in this investigation, including journals and conference publications, annual reports, Philips Company websites, the internet, scholarly articles, and social media reviews. On the company, a SWOT analysis was performed. Findings/Results: The 131-year-old company’s growth as an electrical and electronic goods manufacturer has been meritorious. The company has ventured into the healthcare sector after 2010 and has a road ahead to become a pioneer in this sector. Conclusion: Philips Healthcare is a global player in the manufacture of healthcare equipment. The company has a robust R&D division which can aid in building more innovative healthcare products. By being more innovative the company can achieve its mission of improving global health and sustainability through technological advancements. Paper Type: Company analysis as a Research Case Study
Purpose: Coronary heart disease and the risk of having a heart attack have both risen in recent years. Angioplasty, lifestyle changes, stent implantation, and medications are only some of the methods used to diagnose and treat various diseases. In this study, we will gather and analyze a variety of health indicators in order to identify heart-related illnesses via Machine Learning and Deep Learning prediction models. The best way to improve treatment and mortality prevention is to identify the relevant critical parameters and use Machine Learning or Deep Learning algorithms to achieve optimum accuracy. Design/Methodology/Approach: Secondary sources were used for this investigation. These included periodicals, papers presented at conferences, online sources, and scholarly books and articles. In order to analyze and present the data gathered from academic journals, websites, and other sources, the SWOT analysis is being used. Findings/Results: Predicting heart problems and their severity with a handful of crucial characteristics can save lives. Machine Learning algorithms such as Linear Regression, Deep Learning algorithms such as Neural Networks, and many others can all be applied to those medical parameters for this goal. Originality/Value: This literature study utilizes secondary data collected from diverse sources. Understanding the many types of coronary artery disease and evaluating the most recent advances in predicting the same using Machine Learning approaches will be facilitated by the learned knowledge. This knowledge will aid in the development of a new model or the enhancement of an existing model for predicting coronary artery disease in an individual. Included are tables detailing the forms of coronary artery disease, a variety of recently published research publications on the topic, and standard datasets. Paper Type: Literature Review
Background/Purpose: We have seen an increase in coronary heart disease and heart attack risk in recent years. This is a case study on Sri Jayadeva Institute of Cardiovascular Sciences and Research, Bengaluru to get a better understanding of the heart related ailments and their related symptoms. The hospital specializes in cardiology, cardiothoracic surgery and paediatric cardiology. Based on the symptoms various ailments are diagnosed and treated with different treatments like angioplasty, placement of stent, lifestyle changes and medicines. As part of the research, various health parameters will be collected and analyzed for diagnosing heart related ailments using Machine Learning methods. Determining the appropriate Machine Learning technique to achieve maximum accuracy is the key to achieve a better treatment and prevention of mortality. Design/Methodology/Approach: This study was undertaken using secondary sources, such as website of Sri Jayadeva Institute of Cardiovascular Science and Research, journals, conference articles, the internet and scholarly articles. The SWOT framework is used to analyse, and present, the information acquired from web articles, scholarly papers and other sources. Findings/Results: Heart ailments can be predicted using a few key parameters which can help in avoiding mortality. For this purpose machine learning algorthims, Neural Networks, Particle Swarm algorithm and many more can be applied on those medicial parameters. Originality/Value: This paper reports an exhaustive and comprehensive overview of Coronary Heart Diseases and the treatment provided by Jayadeva Cardiology Hospital on different data collected. Paper Type: Case study-based Research Analysis
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