Proceedings Article10.1109/HSI.2018.8431153
Analysis of Neural Networks Based Heart Disease Prediction System
Rajamhoana Sp,C. Akalya Devi,K. Umamaheswari,R. Kiruba,K. Karunya,R. Deepika +5 more
- 04 Jul 2018
- pp 233-239
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TL;DR: The objective of this paper is to analyze various research works done on heart diseases prediction and classification using various machine learning and deep learning techniques and to conclude which techniques are effective and accurate.
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Abstract: Heart disease is one of the major reason for increase in the death rate. Healthcare is one amongst the most important beneficiaries of huge knowledge & analytics. Extracting medical data is progressively becoming more and more necessary for prediction and treatment of high death rate due to heart attack. Terabytes of data are produced every day. Quality services are needed to avoid poor clinical decisions that lead to disastrous consequences. The Hospitals can make use of appropriate decision support systems thus minimizing the cost of clinical tests. Now-a-day hospitals employ hospital information systems to manage the patient data. Enormous amount of data generated by health care industry is not effectively used. Some new approach is necessary to decrease the expense and to predict the heart disease in an easy. The objective of this paper is to analyze various research works done on heart diseases prediction and classification using various machine learning and deep learning techniques and to conclude which techniques are effective and accurate.
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Citations
Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques
TL;DR: This paper proposes a novel method that aims at finding significant features by applying machine learning techniques resulting in improving the accuracy in the prediction of cardiovascular disease with the hybrid random forest with a linear model (HRFLM).
Heart Disease Prediction using Machine Learning Techniques
Sharyu U. Kamble,Vaishnavi S. Jawanjal,Pooja P. Velapure,Priya K. Jadhav,Sanjivani S. Kadam +4 more
- 13 Apr 2019
TL;DR: A survey of various models based on supervised learning algorithms such as Support Vector Machines (SVM), K-Nearest Neighbour (KNN), Naive Bayes, Decision Trees (DT), Random Forest (RF) and ensemble models are discovered extremely prominent among the researchers.
352
A Systematic Review of the Factors Affecting the Artificial Intelligence Implementation in the Health Care Sector
Shaikha F. S. Alhashmi,Muhammad Alshurideh,Barween Al Kurdi,Said A. Salloum +3 more
- 08 Apr 2020
TL;DR: A systematic review for twenty-three research studies published between (2015–2018) was reviewed and analyzed deeply in order to answer the research question for the critical success factors for implementing artificial intelligence (AI) projects within the health sector.
122
Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico
TL;DR: It is shown that ensemble-learning framework based on deep models could overcome the problem of classifying an unbalanced heart disease dataset and can lead to highly accurate models that are adapted for clinical real data and diagnosis use.
114
Deep Learning Methods in Internet of Medical Things for Valvular Heart Disease Screening System
TL;DR: An experiment is performed where blood flow is blocked temporarily and released to observe changes in the surface temperature of the fingertip skin, and the blood supply capability of the heart is assessed indirectly based on the temperature change curve, suggesting the incidence of valvular heart disease.
85
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