I denna studie undersöks individers generella attityder till vårdapplikationer som användermaskininlärning. Datainsamlingen har skett genom både kvalitativa och kvantitativa metodersom kompletterar varandra. Metoderna innefattar en enkätundersökning och två fokusgrupperbaserade på scenario-based design. Teorin är baserad på forskning inom digitaliseringen avvården, bland annat maskininlärning och mHealth, som ligger till grund och stödjerundersökningen. Även teori om attityder och förtroende till digitaliseringen av vården harunderbyggt undersökningen.I slutsatsen framkommer det att det finns en korrelation mellan hög medvetenhet och positivinställning när det kommer det användandet av vårdapplikationer med maskininlärning. Dengenerella attityden till att få en diagnos av maskininlärning är negativ då de flesta föredrar att fåen diagnos förmedlad av en läkare. Studien indikerar på att detta kan bero på att patienternasöker empati från vården, vilket artificiell intelligens saknar. Förtroendet för en vårdapplikationgrundar sig främst i ryktet om den men även i vilket företag eller organisation som liggerbakom. Studien indikerar på att individer är positivt inställda till att bidra med privat hälsodatatill en vårdapplikation om det leder till förebyggande av sjukdom. Studien ger även en antydanpå att det finns en rädsla kring var privata hälsodata hamnar när den har lämnats ut. / This study aims to research on individuals’ general attitudes towards healthcare applicationsthat use machine learning. The data collection has taken place through both qualitative andquantitative methods as a complement to each other. The methods include a questionnairesurvey, two focus groups based on scenario-based design. The theory is based on research in thedigitalisation of healthcare, including machine learning and mHealth, which is based andsupports the investigation. The theory of attitudes and confidence in the digitalisation of carealso forms the basis for the study.The conclusion shows that there is a correlation between high awareness and positive attitudewhen it comes to the use of healthcare applications with machine learning. The general attitudetowards a diagnosis from machine learning is negative since most people prefer to get adiagnosis mediated by a doctor. The study indicates that this may be because the patients seekempathy from the healthcare system, which artificial intelligence lacks of. Trust towards ahealthcare application is based primarily on the reputation of it, but also in which company ororganization that is behind it. The respondents in the survey are positive about contributing withtheir personal data to a healthcare application if it leads to a prevention of a disease. The studyalso gives an indication that there is a fear of what happens with private health data.

In today’s research and market, IT applications for health-care are gaining huge interest of both IT and medical researchers. Cardiovascular diseases (CVDs) are considered the largest cause of death for both men and women regardless of ethnic backgrounds. More efficient treatments and most importantly efficient methods of cardiac diagnosis that examine heart diseases are desired. Electrocardiography (ECG) is an essential method used to diagnose heart diseases. However, diagnosing any cardiovascular disease based on the 12-lead ECG printout from an ECG machine using human eye might seriously impair analysis accuracy. To meet this challenge of today’s ECG analysis methodology, a more reliable solution that can analyze huge amount of patient’s data in real-time is desired. The software solution presented in this article is aimed to reduce the risk while diagnosing cardiovascular diseases (CVDs) by human eye, computation of large-scale patient’s data in real-time at the patient’s location and sending the required results or summary to the doctor/nurse. Keeping in mind the importance of real-time analysis of patient’s data, the software system has built upon small individual algorithms/modules designed for multi-core architecture, where each module is supposed to be processed by an individual core/processor in parallel. All the input and output processes to the analysis system are made automated, which reduces operator’s interaction to the system and thus reducing the cost. The outputs/results of the processing are summarized to smaller files in both ASCII and binary formats to meet the requirement of exchanging the data over Voice and Data Networks.