Heart attack mortality prediction: an application of machine learning methods

Salman, Issam

doi:10.3906/elk-1811-4

Cited by 15 publications

(5 citation statements)

References 7 publications

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“…Model-specific approaches focused on global interpretability of ML-based models in healthcare have been in use for more than two decades. Due to their high level of interpretability and simple use in practice, the approaches like linear regression or naive Bayes models are still used in different fields of healthcare like urology (Otunaiya & Muhammad, 2019;Zhang, Ren, Ma, & Ding, 2019), toxicology Zhang, Ma, Liu, Ren, & Ding, 2018), endocrinology (Alaoui, Aksasse, & Farhaoui, 2019), neurology , cardiology (Doshi-Velez & Kim, 2018;Feeny et al, 2019;Salmam, 2019), or psychiatry (Guimarães, Araujo, Araujo, Batista, & de Campos Souza, 2019;Obeid et al, 2019). However, even linear regression or naive Bayes models are only interpretable to some extent as it is difficult to interpret the results of such models in case of nonlinearity or nonhomogeneous attributes.…”

Section: Applications Of Interpretable ML In Healthcarementioning

confidence: 99%

Interpretability of machine learning‐based prediction models in healthcare

Štiglic

Kocbek

Fijačko

et al. 2020

WIREs Data Min & Knowl

290

148

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There is a need of ensuring that learning (ML) models are interpretable. Higher interpretability of the model means easier comprehension and explanation of future predictions for end-users. Further, interpretable ML models allow healthcare experts to make reasonable and data-driven decisions to provide personalized decisions that can ultimately lead to higher quality of service in healthcare. Generally, we can classify interpretability approaches in two groups where the first focuses on personalized interpretation (local interpretability) while the second summarizes prediction models on a population level (global interpretability). Alternatively, we can group interpretability methods into model-specific techniques, which are designed to interpret predictions generated by a specific model, such as a neural network, and model-agnostic approaches, which provide easy-to-understand explanations of predictions made by any ML model. Here, we give an overview of interpretability approaches using structured data and provide examples of practical interpretability of ML in different areas of healthcare, including prediction of healthrelated outcomes, optimizing treatments, or improving the efficiency of screening for specific conditions. Further, we outline future directions for interpretable ML and highlight the importance of developing algorithmic solutions that can enable ML driven decision making in high-stakes healthcare problems.

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Section: Applications Of Interpretable ML In Healthcarementioning

confidence: 99%

Interpretability of machine learning‐based prediction models in healthcare

Štiglic

Kocbek

Fijačko

et al. 2020

WIREs Data Min & Knowl

290

148

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“…The second dataset [15] contains information from a total of 787 patients from two different countries, Czechia and Syria. Among these patients, 603 individuals are from the Czech Republic, while the remaining 184 patients originate from Syria.…”

Section: Methodsmentioning

confidence: 99%

Development and Performance Evaluation of a Novel Bayesian Network Model for the Classification of Heart Disease

Salman,

Panda,

Vomlel

2023

Preprint

Self Cite

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Background: Acute myocardial infarction (AMI) is the leading cause of death world- wide and requires accurate models to predict the risk of heart disease and mortality from these accidents for early intervention and prevention. Objective: Bayesian networks (BNs) as a class of machine learning will not only significantly improve the prediction of heart disease outcomes and its mortality, but also represent the dependence/independence between heart abnormalities, mortality and important markers. It allows analysis of healthcare quality and cost reduction. Results: The performance of specific models was evaluated based on a comprehensive review of the existing literature, including the techniques, algorithms, and data sources used in specific studies. These findings demonstrated that machine learning has the potential to improve the assessment of heart diseases and AMI by using BNs. it shows the impact of different variables on the models’ performance. Conclusion: Bayesian networks have redefined how we approach complex decision- making. Their ability to combine data-driven insights with probabilistic reasoning makes them essential tools for addressing classification challenges across multiple domains. As technology continues to advance, these models are likely to play an increasingly important role in improving accuracy, efficiency and decision making.

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“…Salman I. [17] 787 hasta ve 24 değişken içeren veri setini kullandığı çalışmasında NB, DT ile güçlendirilmiş Naive Bayes (TAN) ve TAN ve Chow-Liu (TANI) karşılaştırması yaparak kalp hastalıkları tahmini yapmayı amaçlamıştır. Sonuçta TANI metodunun diğerlerine kıyasla en yüksek doğruluk oranına sahip olduğu sonucuna ulaşmıştır.…”

Section: Kaynak Araştirmasi (Literature Survey)unclassified

Hiperparametre ayarlama ve veri dengelemenin kalp hastalığı tahmini için kullanılan makine öğrenimi algoritmaları üzerindeki etkilerinin incelenmesi

SUNGUR,

BAKIR

2024

Bilişim Teknolojileri Dergisi

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Kalp hastalığı belirtilerinin ihmal edilmesi ciddi rahatsızlıklarla hatta ölümle sonuçlanabilir. Makine öğrenme teknikleri ile ön tanı için bu belirtiler kullanılarak kişide kalp hastalığı olup olmadığına dair tahmin yapılabilmektedir. Bu çalışmada Logistic Regression, Decision Trees, Random Forest, K Nearest Neighbors, Naive Bayes, Gradient Boosting, XGBoost ve Bagging algoritmaları ile kalp hastalığı tahmini yapılmıştır. SMOTE, SMOTETomek, Oversample Minority Class, Undersample Majority Class veri dengeleme yöntemleri ile dört ayrı veri seti oluşturulmuştur. Seçilen tüm makine öğrenme algoritmalarına Random Search ve Bayesian Optimizasyon teknikleriyle hiper parametre optimizasyonu yapılarak sonuçlar karşılaştırılmıştır. Veri dengeleme ve hiper parametre optimizasyonunun kalp hastalığının tahmininde kullanılan makine öğrenme teknikleri performansına etkisi karşılaştırılarak literatüre özgün bir çalışma kazandırılmıştır. Çalışmada Amerika Birleşik Devletleri’nde 319.795 kişi ile yapılan 20 öz nitelikli bir anket olan veri seti kullanılmıştır. Random Forest algoritması SMOTETomek veri dengeleme tekniği kullanılarak ve Bayesian hiper parametre optimizasyonu yapılarak oluşturulan modelde %94 tahmin başarısı elde edilmiştir. Ayrıca, Random Forest algoritması ile Oversample Minority Class veri dengeleme tekniği kullanılarak ve Bayesian hiper parametre optimizasyonu yapılarak %97 sınıflandırma doğruluğu elde edilmiştir.

show abstract

Heart attack mortality prediction: an application of machine learning methods

Cited by 15 publications

References 7 publications

Interpretability of machine learning‐based prediction models in healthcare

Interpretability of machine learning‐based prediction models in healthcare

Development and Performance Evaluation of a Novel Bayesian Network Model for the Classification of Heart Disease

Hiperparametre ayarlama ve veri dengelemenin kalp hastalığı tahmini için kullanılan makine öğrenimi algoritmaları üzerindeki etkilerinin incelenmesi

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