Identifying the Risk of Cardiovascular Diseases From the Analysis of Physiological Attributes

Mostafa, Nafis; Azim, Muhammad Anwarul; Kabir, Rayhan; Ajwad, Rasif

doi:10.1109/tensymp50017.2020.9230887

Cited by 7 publications

(2 citation statements)

References 10 publications

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“…As a result, maintaining proper heart function is critical. Cardiovascular disease (CVD), also known as heart disease, has risen to become one of the world's leading causes of death [2]. According to the World Health Organization, heart disease kills 17.7 million people each year are accounting for 31% of all deaths worldwide (WHO).…”

Section: Introductionmentioning

confidence: 99%

Cardiovascular Disease Prediction Using ML and DL Approaches

Suneetha¹,

Mahalngam

2022

IJRITCC

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Healthcare is very important aspects of human life. Cardiovascular disease, also known as the coronary artery disease, is one of the many deadly infections that kill people in India and around the world. Accurate predictions can prevent heart disease, but incorrect predictions can be fatal. Therefore, here this paper describes a method for predicting cardiovascular disease that makes use of Machine Learning (ML) and Deep Learning (DL). In this paper, SMOTE-ENN (Synthetic Minority Oversampling Technique Edited Nearest Neighbor) was used to equalize the distribution of training data. The K-Nearest Neighbor method (KNN), Naive Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), XGBoost (Extreme Gradient Boosting), Artificial Neutral Network (ANN), and Convolutional Neutral Network (CNN) are among the classifiers used in this paper. From Public Health Dataset required data is collected and focused on recognizing the best approach for predicting the disease in preliminary phase. This experiment end results show that the use of Artificial Neural Networks can be of much useful in prediction with better accuracy (95.7%) than compared to any other ML approaches.

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Section: Introductionmentioning

confidence: 99%

Cardiovascular Disease Prediction Using ML and DL Approaches

Suneetha¹,

Mahalngam

2022

IJRITCC

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“…Penyakit kardiovaskular menjadi ancaman bagi penduduk dunia karena terbukti menjadi salah satu penyebab kematian yang terbilang cukup tinggi [1]. Berdasarkan hasil survei dari WHO (World Health Organization), penyakit kardiovaskular (CVD) menjadi penyebab kematian primer dengan data sekitar 18 juta kasus banyaknya [2]. Hampir sebanyak 31% kematian didunia disebabkan oleh penyakit ini [3].…”

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Prediksi Pasien Dengan Penyakit Kardiovaskular Menggunakan Random Forest

Anshori¹,

Rikatsih²,

Haris³

2023

tektrika

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Cardiovascular disease is one of the deadliest diseases in the world. This is evidenced by data released by WHO which shows around 18 million deaths. This disease causes the cessation of the heartbeat which is the main source of life for the human body.This disease is caused by various things including an unhealthy lifestyle. Examples are consuming cigarettes and alcohol. In addition, it is also caused by other factors, namely health problems such as high blood pressure, cholesterol, diabetes, depression, or anxiety. The cardiovascular disease tends to be difficult to cure, therefore a precise and accurate prediction is needed in diagnosing patients. One method of making predictions is using machine learning techniques. In machine learning, there are various methods that can be used, one of which is the decision tree-based method, namely random forest. Before the random forest is implemented to create a model, the data is pre-processed by normalizing and applying cross-validation with k-fold = 10. The prediction results with the random forest in this study provide an accuracy of 98%. This accuracy is higher when compared to previous studies with the same dataset, namely 96.75% using the ensemble method and 91.61% with logistic regression. On this basis, it proves that the random forest can be used to predict cardiovascular disease. Key Words: cardiovascular disease, tree model, random forest, machine learning.

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