PENERAPAN METODE REGRESI LOGISTIK BINER UNTUK MENGKLASIFIKASI RISIKO PENYAKIT JANTUNG

GUMARA, JEKTA and Zayanti, Des Alwine and Kresnawati, Endang Sri (2021) PENERAPAN METODE REGRESI LOGISTIK BINER UNTUK MENGKLASIFIKASI RISIKO PENYAKIT JANTUNG. Undergraduate thesis, Sriwijaya University.

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Abstract

The heart is a human organ that plays a role in the circulatory system. Heart disease is a condition in which the heart cannot do its job properly. Heart disease is a disease that plays a major role as the number one cause of death worldwide. In this study, the risk of heart disease will be predicted based on age, gender, type of chest pain, lower limit blood pressure, cholesterol, fasting blood sugar, electrocardiography results, maximum heart rate, exercise that induces pain, depression induced by strenuous exercise, peak slope. ST segment, the number of major blood vessels and the type of vascular damage to be predicted in the sick or healthy group. This study aims to apply the binary logistic regression method in predicting the risk of heart disease. Based on the calculation results, the variables that affect the risk of heart disease are gender, chest pain, ECG (electrocardiogram) results, number of heartbeats, ST segments obtained from exercise relative to rest and heart status and the average level of overall classification accuracy with cross varidation of 49.38%. Keywords : Heart Disease, Classification, Binary Logistic Regression.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Heart Disease, Classification, Binary Logistic Regression.
Subjects:	Q Science > QA Mathematics > QA273-280 Probabilities. Mathematical statistics > QA279.C663 Response surfaces (Statistics) Q Science > QA Mathematics > QA273-280 Probabilities. Mathematical statistics > QA279.M67 Experimental design. Response surfaces (Statistics)
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	Jekta Gumara
Date Deposited:	27 Aug 2021 02:59
Last Modified:	27 Aug 2021 02:59
URI:	http://repository.unsri.ac.id/id/eprint/52736

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