首页|Gdansk University of Technology Reports Findings in Heart Failure [A machine learning approach to classifying New York Heart Association (NYHA) hea rt failure]
Gdansk University of Technology Reports Findings in Heart Failure [A machine learning approach to classifying New York Heart Association (NYHA) hea rt failure]
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Heart Disorders and Di seases - Heart Failure is the subject of a report. According to news reporting o ut of Gdansk, Poland, by NewsRx editors, research stated, “According to the Euro pean Society of Cardiology, globally the number of patients with heart failure n early doubled from 33.5 million in 1990 to 64.3 million in 2017, and is further projected to increase dramatically in this decade, still remaining a leading cau se of morbidity and mortality. One of the most frequently applied heart failure classification systems that physicians use is the New York Heart Association (NY HA) Functional Classification.” Our news journalists obtained a quote from the research from the Gdansk Universi ty of Technology, “Each NYHA class describes a patient’s symptoms while performi ng physical activities, delivering a strong indicator of the heart performance. In each case, a NYHA class is individually determined routinely based on the sub jective assessment of the treating physician. However, such diagnosis can suffer from bias, eventually affecting a valid assessment. To tackle this issue, we ta ke advantage of the machine learning approach to develop a decision-tree, along with a set of decision rules, which can serve as additional blinded investigator tool to make unbiased assessment. On a dataset containing 434 observations, the supervised learning approach was initially employed to train a Decision Tree mo del. In the subsequent phase, ensemble learning techniques were utilized to deve lop both the Voting Classifier and the Random Forest model. The performance of a ll models was assessed using 10-fold cross-validation with stratification.The De cision Tree, Random Forest, and Voting Classifier models reported accuracies of 76.28%, 96.77%, and 99.54 % respectively. The Voting Classifier led in classifying NYHA I and III with 98.7% and 100% accuracy. Both Random Forest and Voting Classifier flawle ssly classified NYHA II at 100%. However, for NYHA IV, Random Fores t achieved a perfect score, while the Voting Classifier reported 90% . The Decision Tree showed the least effectiveness among all the models tested. In our opinion, the results seem satisfactory in terms of their supporting role in clinical practice. In particular, the use of a machine learning tool could re duce or even eliminate the bias in the physician’s assessment.”
GdanskPolandEuropeCardiologyCard iovascular Diseases and ConditionsCyborgsEmerging TechnologiesHealth and M edicineHeart DiseaseHeart Disorders and DiseasesHeart FailureMachine Lea rning
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