A Comparative Analysis of Machine Learning Classifiers for Robust Heart Disease Prediction

Thakkar, Hiren Kumar; Shukla, Hrushikesh; Patil, Sonali

doi:10.1109/indicon49873.2020.9342444

Cited by 16 publications

(4 citation statements)

References 14 publications

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“…An improved version of the K-means neighbor classifier has been used to guarantee more accuracy in predicting heart disease early on [44]. Comparative analysis of ML classifiers such as LR, Naive Bayes, Random Forest, SVM, and KNN has been conducted to evaluate their performance in heart disease prediction [45]. Dimensionality reduction techniques, such as feature selection methods, improve classification accuracy by identifying and removing redundant and irrelevant symptoms from the data set [46].…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Heart Disease Detection Using Convolutional Neural Networks and Classic Machine Learning Methods

Mulyani,

Wijaya,

Trinidya

2024

COMPLETE

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This study addresses the problem of heart disease detection, a critical concern in public health. The research aims to compare the performance of Convolutional Neural Networks (CNN) with conventional machine learning algorithms in diagnosing heart disease using a dataset comprising 14 features. The primary objective is to determine whether CNNs can provide more accurate and reliable results than traditional techniques. The research employs rigorous preprocessing, normalizing relevant features, and splits the dataset into an 80-20 training-testing split. The model is trained for 300 epochs with a batch size of 64, and performance evaluation is conducted using confusion matrices and classification reports. The results reveal that the CNN model achieved a remarkable accuracy of 100%, demonstrating its potential to outperform conventional machine learning algorithms. These findings emphasize the significance of deep learning techniques in improving heart disease diagnostics, although further research is needed to optimize CNN models and address interpretability concerns for practical implementation in healthcare settings.

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Section: Literature Reviewmentioning

confidence: 99%

Enhancing Heart Disease Detection Using Convolutional Neural Networks and Classic Machine Learning Methods

Mulyani,

Wijaya,

Trinidya

2024

COMPLETE

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“…In [12], To make a reliable prediction of heart disease, machine learning classifiers are created and a comparison analysis is done. Five ML algorithms are developed, and the Cleveland Heart Disease Data set is used to thoroughly assess each one's performance.…”

Section: Related Workmentioning

confidence: 99%

Heart disease classification using optimized Machine learning algorithms

Kadhim

Radhi

2023

ijcsm

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Early detection of heart disease is exceptionally critical to saving the lives of human beings. Heart attack is one of the primary causes of high death rates throughout the world, due to the lack of human and logistical resources in addition to the high costs of diagnosing heart diseases which plays a key role in the healthcare sector, this model is suggested. In the field of cardiology, patient data plays an essential role in the healthcare system. This paper presents a proposed model that aims to identify the optimal machine learning algorithm that can predict heart attacks with high accuracy in the early stages. The concepts of machine learning are used for training and testing the model based on the patient's data for effective decision-making. The proposed model consists of three stages, the first stage is patient data collection and processing, and the second stage is data training and testing using machine learning algorithms Random Forest, Support Vector Machines, K-Nearest Neighbor, and Decision Tree) that show The best classification (94.958 percent) with the Random Forest algorithm and the third stage is optimized the classification results using one of the hyperparameters optimization techniques random search that shows The best accuracy was (95.4 percent) obtained also with RF

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“…Various academicians and researchers throughout the world have undertaken numerous quality oriented works related to healthcare prediction (12,13) across different domains (14,15) using recent technologies. Researchers have also analyzed psychological health disorders using many predictive learning models.…”

Section: Related Workmentioning

confidence: 99%

An Explainable Intelligence Driven Query Prioritization Using Balanced Decision Tree Approach for Multi-Level Psychological Disorders Assessment

Mishra

Tripathy

Thakkar

et al. 2021

Front. Public Health

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Human emotions affect psychological health to a great level. Positive emotions relate to health improvement; whereas negative emotions may aggravate psychological disorders such as anxiety, stress, and depression. Although there exist several computational methods to predict psychological disorders, most of them provide a black-box view of uncertainty. This research involves developing a novel predictive model for multi class psychological risk recognition with an accurate explainable interface. Standard questionnaires are utilized as data set and a new approach called a Q-Prioritization is employed to drop insignificant questions from the data set. Moreover, a novel balanced decision tree method based on repetitive oversampling is applied for the training and testing of the model. Predictive nature along with its contributing factors are interpreted with three techniques such as permuted feature importance, contrastive explanation, and counterfactual method, which together form a reasoning engine. The prediction outcome generated an impressive performance with an aggregated accuracy of 98.25%. The mean precision, recall, and F-score metric recorded were 0.98, 0.977, and 0.979, respectively. Also, it was noted that without applying Q-Prioritization, the accuracy significantly drops to 90.25%. The error rate observed with our model was only 0.026. The proposed multi-level psychological disorder predictive model can successfully serve as an assistive deployment for medical experts in the effective treatment of mental health.

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A Comparative Analysis of Machine Learning Classifiers for Robust Heart Disease Prediction

Cited by 16 publications

References 14 publications

Enhancing Heart Disease Detection Using Convolutional Neural Networks and Classic Machine Learning Methods

Enhancing Heart Disease Detection Using Convolutional Neural Networks and Classic Machine Learning Methods

Heart disease classification using optimized Machine learning algorithms

An Explainable Intelligence Driven Query Prioritization Using Balanced Decision Tree Approach for Multi-Level Psychological Disorders Assessment

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