Predict the Diagnosis of Heart Disease Patients Using Classification Mining Techniques

Prediction of a cardiovascular diseases has always a tedious challenge for doctors and medical practitioners. Most of the practitioners and hospital staff offers expensive medication, care and surgeries to treat the cardiovascular patients. At early-stage of prediction of heart-oriented problems will be giving a chance of survival by taking necessary precautions. Over the years there are different types of methodologies were proposed to predict the cardiovascular diseases one of the best methodologies is a Machine learning approach. These years many scientific advancements take place in the Artificial Intelligence, Machine learning, and Deep learning which gives an extra push up to help and implement the path in the field of medical image processing and medical data analysis. By using the enormous dataset from various medical experts used to help the researchers to predict the coronary problems prior to happening. Many researchers have tried and implemented different machine learning algorithms to automate the prediction analysis using the enormous number of datasets. There are numerous algorithms and procedures to predict the cardiovascular diseases and accessible to be specific Classification methods including Artificial Neural Networks (AI), Decision tree (DT), Support vector machine (SVM), Genetic algorithm (GA), Neural network (NN), Naive Bayes (NB) and Clustering algorithms like K-NN. A few examinations have been done for creating expectation models utilizing singular procedures and additionally concatenating at least two strategies. This paper gives a speedy and simple survey and knowledge of approachable prediction models using different researchers work from 2004 to 2019. The examination indicates the precision of individual experiments done by various researchers.

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“…The hidden-layers has gotten before Drop rate measure. The exploratory outcomes accomplished with 90.0% of precision [5].…”

Section: Literature Surveymentioning

confidence: 99%

A Survey on Cardiovascular Prediction using Variant Machine learning

Chandrika¹,

Madhavi²,

Sindhuja³

et al. 2021

E3S Web Conf.

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“…Fewer numbers of attributes may reduce human cognitive burden by reducing the number of possible options. Patel et al (2013) and Anbarasi et al (2010) in their work reduced the number of attributes to predict heart abnormalities in cardiac patients using data mining and genetic algorithms respectively. Heart abnormality prediction attributes are reduced from thirteen to six.…”

Section: Employment Of Machine Learning In Cardiac Abnormality Predic...mentioning

confidence: 99%

ANN based Expert System to Predict Disease in Cardiac Patients at Initial Stages

Rashid

2015

International Journal of E-Health and Medical Communications

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Objective of this research is to develop an expert system for the preliminary investigation of cardiac abnormality in human beings. Artificial Neural Network (ANN) is judged best for the prediction of heart abnormalities in cardiac patients at initial stages. Our research is intended to employ an Artificial Intelligence (AI) technique in an automated solution, having minimum error bounds. An ANN based expert system is designed and developed, which identifies presence or absence of cardiac disease in patients by considering best practiced disease symptoms. The proposed expert system may help the clinicians in the preliminary investigation of cardiac abnormality in human beings.

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“…Another approach is that of [12], who proposed a model to predict heart disease from a set of private data, reducing the amount of features from 14 to 6 using a genetic algorithm that allows the selection of categorical features. They subsequently used traditional classifiers for the prediction and diagnosis of heart disease, obtaining a classification percentage of 99.2% using the decision tree technique and 96.5% using the naïve Bayes technique.…”

Section: Introductionmentioning

confidence: 99%

Minimum Relevant Features to Obtain Explainable Systems for Predicting Cardiovascular Disease Using the Statlog Data Set

et al. 2021

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Learning systems have been focused on creating models capable of obtaining the best results in error metrics. Recently, the focus has shifted to improvement in the interpretation and explanation of the results. The need for interpretation is greater when these models are used to support decision making. In some areas, this becomes an indispensable requirement, such as in medicine. The goal of this study was to define a simple process to construct a system that could be easily interpreted based on two principles: (1) reduction of attributes without degrading the performance of the prediction systems and (2) selecting a technique to interpret the final prediction system. To describe this process, we selected a problem, predicting cardiovascular disease, by analyzing the well-known Statlog (Heart) data set from the University of California’s Automated Learning Repository. We analyzed the cost of making predictions easier to interpret by reducing the number of features that explain the classification of health status versus the cost in accuracy. We performed an analysis on a large set of classification techniques and performance metrics, demonstrating that it is possible to construct explainable and reliable models that provide high quality predictive performance.

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Predict the Diagnosis of Heart Disease Patients Using Classification Mining Techniques

Cited by 30 publications

References 12 publications

A Survey on Cardiovascular Prediction using Variant Machine learning

A Survey on Cardiovascular Prediction using Variant Machine learning

ANN based Expert System to Predict Disease in Cardiac Patients at Initial Stages

Minimum Relevant Features to Obtain Explainable Systems for Predicting Cardiovascular Disease Using the Statlog Data Set

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