Coping ways of the medical cost in Ischemic Heart Disease patients of Bangladesh

Banu, Fatema Akhter; Yasmeen, BH Nazma; Parvez, Shahriar; Hossain, SK Akbar

doi:10.3329/nimcj.v9i1.35923

Cited by 1 publication

(1 citation statement)

References 8 publications

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“…In the case of developing countries such as Bangladesh, according to the National Heart Foundation of Bangladesh-Package Charge of Cardiac Operation (effective from 01 November 2019): Coronary Artery Bypass Graft (CABG)-2,10,000/-; Atrial Septal Defect (ASD)-1,50,000/-; CMC/PDA (Closed Heart Surgery)-60,000/-; Valve Replacement: 1,45,000 + Cost of the Single Valve & 1,50,000 + Cost of the Double Valve; CABG + ASD/VSD-2,10,000 + 50,000; Intensive Care Unit (ICU) with Ventilator Charge (Per day)-8,000/-etc. [2].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning and Artificial Neural Network for Predicting Heart Failure Risk

Rahman¹,

Rifat²,

Chy³

et al. 2023

Computer Systems Science and Engineering

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Heart failure is now widely spread throughout the world. Heart disease affects approximately 48% of the population. It is too expensive and also difficult to cure the disease. This research paper represents machine learning models to predict heart failure. The fundamental concept is to compare the correctness of various Machine Learning (ML) algorithms and boost algorithms to improve models' accuracy for prediction. Some supervised algorithms like K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF), Logistic Regression (LR) are considered to achieve the best results. Some boosting algorithms like Extreme Gradient Boosting (XGBoost) and Cat-Boost are also used to improve the prediction using Artificial Neural Networks (ANN). This research also focuses on data visualization to identify patterns, trends, and outliers in a massive data set. Python and Scikit-learns are used for ML. Tensor Flow and Keras, along with Python, are used for ANN model training. The DT and RF algorithms achieved the highest accuracy of 95% among the classifiers. Meanwhile, KNN obtained a second height accuracy of 93.33%. XGBoost had a gratified accuracy of 91.67%, SVM, CATBoost, and ANN had an accuracy of 90%, and LR had 88.33% accuracy.

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Section: Introductionmentioning

confidence: 99%