- Tytuł:
- Prediction of mortality rates in heart failure patients with data mining methods
- Autorzy:
-
Bohacik, J.
Kambhampati, C.
Davis, D. N.
Cleland, J. G. F. - Powiązania:
- https://bibliotekanauki.pl/articles/908867.pdf
- Data publikacji:
- 2013
- Wydawca:
- Uniwersytet Marii Curie-Skłodowskiej. Wydawnictwo Uniwersytetu Marii Curie-Skłodowskiej
- Tematy:
-
heart failure
data mining
prediction of mortality rates
home telemonitoring
Bayesian network method
decision tree method
neural network method
nearest neighbour method - Opis:
- Heart failure is one of the severe diseases which menace the human health and affect millions of people. Half of all patients diagnosed with heart failure die within four years. For the purpose of avoiding life-threatening situations and minimizing the costs, it is important to predict mortality rates of heart failure patients. As part of a HEIF-5 project, a data mining study was conducted aiming specifically at extracting new knowledge from a group of patients suffering from heart failure and using it for prediction of mortality rates. The methodology of knowledge discovery in databases is analyzed within the framework of home telemonitoring. Several data mining methods such as a Bayesian network method, a decision tree method, a neural network method and a nearest neighbour method are employed. The accuracy for the data mining methods from the point of view of avoiding life-threatening situations and minimizing the costs is discussed. It seems that the decision tree method achieves the best accuracy results and is also interpretable for the clinicians.
- Źródło:
-
Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica; 2013, 13, 1; 7-16
1732-1360
2083-3628 - Pojawia się w:
- Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica
- Dostawca treści:
- Biblioteka Nauki
Artykuł