Heart attack mortality prediction: an application of machine learning methods

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F19%3A00332898" target="_blank" >RIV/68407700:21340/19:00332898 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.3906/elk-1811-4" target="_blank" >https://doi.org/10.3906/elk-1811-4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3906/elk-1811-4" target="_blank" >10.3906/elk-1811-4</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Heart attack mortality prediction: an application of machine learning methods

Popis výsledku v původním jazyce

The heart is an important organ in the human body, and Acute Myocardial Infarction (AMI) is the leading 5 cause of death in most countries. Researchers are diverting a lot of data analysis work to assist doctors in predicting 6 the heart problem. An analysis of the data related to dierent health problems and its functions can help in predicting 7 with a degree of certainty the wellness of this organ. Our research reported in this paper is twofold. In the rst part 8 of the paper, we compare dierent predictive models of hospital mortality for patients with AMI. All results presented 9 in this part are based on real data of about 603 patients from a hospital in Czechia and about 184 patients from two 10 hospitals in Syria. Although the learned models may be specic to the data, we also draw more general conclusions 11 that we believe are generally valid. In the second part of the paper, because the data is incomplete and imbalanced 12 we develop the Chow-Liu and tree-augmented naive Bayesian (TAN) to deal with that data in better conditions, and 13 measure the quality of these algorithms with other algorithms.

Název v anglickém jazyce

Heart attack mortality prediction: an application of machine learning methods

Popis výsledku anglicky

The heart is an important organ in the human body, and Acute Myocardial Infarction (AMI) is the leading 5 cause of death in most countries. Researchers are diverting a lot of data analysis work to assist doctors in predicting 6 the heart problem. An analysis of the data related to dierent health problems and its functions can help in predicting 7 with a degree of certainty the wellness of this organ. Our research reported in this paper is twofold. In the rst part 8 of the paper, we compare dierent predictive models of hospital mortality for patients with AMI. All results presented 9 in this part are based on real data of about 603 patients from a hospital in Czechia and about 184 patients from two 10 hospitals in Syria. Although the learned models may be specic to the data, we also draw more general conclusions 11 that we believe are generally valid. In the second part of the paper, because the data is incomplete and imbalanced 12 we develop the Chow-Liu and tree-augmented naive Bayesian (TAN) to deal with that data in better conditions, and 13 measure the quality of these algorithms with other algorithms.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Turkish Journal of Electrical Engineering & Computer Sciences

ISSN

1300-0632

e-ISSN

1303-6203

Svazek periodika

27

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

TR - Turecká republika

Počet stran výsledku

12

Strana od-do

4378-4389

Kód UT WoS článku

000506165400025

EID výsledku v databázi Scopus

2-s2.0-85076636366