Prediction of Cardiovascular Disease Using Machine Learning Algorithms

Dinesh, K.; Arumugaraj, K; Santhosh, Kumar D; Mareeswari, V.

doi:10.1109/icctct.2018.8550857

Cited by 139 publications

(43 citation statements)

References 7 publications

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“…The proposed algorithm, Decision Tree obtained 75.55% accuracy. Dinesh et al [48] examined 920 datasets (Cleveland, Long Beach VA, Switzerland, and Hungarian) which from the UCI machine learning repository. Random forest achieved 80.89% accuracy; on the other hand, Saqlain has received 68.6% accuracy over the AFIC dataset [56].…”

Section: Literature Reviewmentioning

confidence: 99%

“…That analysis showed that the Decision Tree achieved 75.55% accuracy. Dinesh et al [48] worked on a 920-records datasets, combining the Cleveland, Long Beach VA, Switzerland and Hungarian datasets from the UCI repository and showed that RF could obtain an accuracy of 80.89%. Other authors in [56] applied the DT and RF to a dataset of 500 which was taken from the Armed Forces Institute of Cardiology (AFIC) and reported that DT achieved the best result (86.6 %).…”

Section: C) Comparison Table Between the Accuracy Of The Proposed Modmentioning

confidence: 99%

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Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques

et al. 2021

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Cardiovascular diseases are among the most common serious illnesses affecting human health. CVDs may be prevented or mitigated by early diagnosis, and this may reduce mortality rates. Identifying risk factors using machine learning models is a promising approach. We would like to propose a model that incorporates different methods to achieve effective prediction of heart disease. For our proposed model to be successful, we have used efficient Data Collection, Data Pre-processing and Data Transformation methods to create accurate information for the training model. We have used a combined dataset (Cleveland, Long Beach VA, Switzerland, Hungarian and Stat log). Suitable features are selected by using the Relief, and Least Absolute Shrinkage and Selection Operator (LASSO) techniques. New hybrid classifiers like Decision Tree Bagging Method (DTBM), Random Forest Bagging Method (RFBM), K-Nearest Neighbors Bagging Method (KNNBM), AdaBoost Boosting Method (ABBM), and Gradient Boosting Boosting Method (GBBM) are developed by integrating the traditional classifiers with bagging and boosting methods, which are used in the training process. We have also instrumented some machine learning algorithms to calculate the Accuracy (ACC), Sensitivity (SEN), Error Rate, Precision (PRE) and F1 Score (F1) of our model, along with the Negative Predictive Value (NPR), False Positive Rate (FPR), and False Negative Rate (FNR). The results are shown separately to provide comparisons. Based on the result analysis, we can conclude that our proposed model produced the highest accuracy while using RFBM and Relief feature selection methods (99.05%).

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Section: Literature Reviewmentioning

confidence: 99%

Section: C) Comparison Table Between the Accuracy Of The Proposed Modmentioning

confidence: 99%

Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques

et al. 2021

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“…Apart from that the aspect of prior knowledge about the data, computational complexity and expected results are also deciding factors, and the correct use of the model is extremely crucial in this regard [31]. Recent research has delved into uniting different techniques to provide hybrid machine learning algorithms [32]. Nevertheless, it is clear that the use of machine learning and computational intelligence takes an active role in predicting the health risks and the probability of diseases using the intelligence hidden in the health data.…”

Section: Data Modeling In Healthcare Toward Predictive Analysis "Datamentioning

confidence: 99%

Nonlinear Systems in Healthcare towards Intelligent Disease Prediction

Chatterjee¹,

Cymberknop²,

Armentano³

2020

Nonlinear Systems -Theoretical Aspects and Recent Applications

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Healthcare is one of the key fields that works quite strongly with advanced analytical techniques for prediction of diseases and risks. Data being the most important asset in recent times, a huge amount of health data is being collected, thanks to the recent advancements of IoT, smart healthcare, etc. But the focal objective lies in making sense of that data and to obtain knowledge, using intelligent analytics. Nonlinear systems find use specifically in this field, working closely with health data. Using advanced methods of machine learning and computational intelligence, nonlinear analysis performs a key role in analyzing the enormous amount of data, aimed at finding important patterns and predicting diseases. Especially in the field of smart healthcare, this chapter explores some aspects of nonlinear systems in predictive analytics, providing a holistic view of the field as well as some examples to illustrate such intelligent systems toward disease prediction.

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“…The study in [23] offered the evaluation and application of the given MLAs which was applied in R programming in producing CVDs prediction. Also, web-based application was also created which provides module for the attributes, test results, references, and graphs for entry.…”

Section: )mentioning

confidence: 99%

CAREdio: Health Screening and Heart Disease Prediction System for Rural Communities in the Philippines

Tolentino¹,

Isoy²,

Bulawan³

et al. 2020

IJACSA

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Cardiovascular diseases cover a large quantity of worldwide disease load, setting it to top leading cause of death. In the Philippines, given the rapid economic advancement and urbanization, the most vulnerable sector has not been impacted by this development. Data from the Philippine Statistical Authority (PSA) in 2016 revealed that of the country's total recorded deaths, six out of ten were medically unattended and of which the largest portion are from the rural population. Consequently, medical analysis is needed to perform effectively and precisely however, most developing countries have limited resources and lack medical expert for specialized field such as cardiologists. The proponents essentially seeks to address the issues Philippine health sector specifically in rural and remote populace by executing efficient and low-cost health screening and diseases prediction system using commercially available medical devices and machine learning algorithms for the prediction of three of the most heart diseases (Hypertension, Heart Attack, Diabetes). The system is composed of CAREdio mobile app, prototype hardware consists of different health sensors and devices, and a machine learning model that is applied to determine the user's individual probability of having a specific heart disease. The machine learning models used were trained using the data gathered from Rosario Reyes Health Center and Ospital ng Sampaloc (Sampaloc Hospital), both located in Manila City, Philippines. CAREdio achieves accuracy values over 0.80 for all diseases. The system can diagnose multiple cardiovascular diseases in a single app that will benefit people rural communities.

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Prediction of Cardiovascular Disease Using Machine Learning Algorithms

Cited by 139 publications

References 7 publications

Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques

Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques

Nonlinear Systems in Healthcare towards Intelligent Disease Prediction

CAREdio: Health Screening and Heart Disease Prediction System for Rural Communities in the Philippines

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