A Computational Intelligence Method for Effective Diagnosis of Heart Disease using Genetic Algorithm

Kumar, P. Siva; Anand, Dama; Kumar, V. Uday; Bhattacharyya, Debnath

doi:10.14257/ijbsbt.2016.8.2.34

Cited by 20 publications

(4 citation statements)

References 17 publications

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“…According to Kumar, Anand et al [4], Genetic Algorithm (GA) technique was described to construct computational intelligence methods for the diagnosis of heart disease. The performance of the model is validated using a 3-fold cross validation approach.…”

Section: Figure 1: Dataset Descriptionmentioning

confidence: 99%

“…These technologies have also been used to combat heart diseases. Other scholars, Mirzajani et al [3], Kumar et al [4], and Enriko et al [5], have used different types of techniques such as Genetic Algorithm (GA) to construct computational intelligence methods for the diagnosis of heart disease and some classification algorithms like, j48 decision tree, Naive Bayes (NB), KNN and SMO were analysed and compared to predict heart disease. In Zambia, heart disease is a major problem, it had contributed to 10% of all deaths in those aged between 30 and 70 in the year 2017 and in year 2020 the death rate increased by 2% [2].…”

Section: Introductionmentioning

confidence: 99%

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Assisted Artificial Intelligence Medical Diagnosis System for Heart Disease

Maambo¹,

Phiri²,

Kalumbilo³

et al. 2022

zictjournal

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In recent years increase of new and effective medical field applications has critical part in research. Artificial Intelligence (AI) Systems has great influence in the growth of these effective medical field applications and tools. One of the major health problems in both established and developing countries is heart disease. Therefore,diagnosis to regulate the heart disease is very vital, so that appropriate actions can be taken. The Artificial Intelligence System uses input medical data collected from an existing dataset from Kaggle and applies this data on the artificial intelligence application developed that uses data mining algorithm and a basic model on Zambian patients to see if the model will predict correctly. From the dataset collected 80% was used as training data and 20% was used as testing data. The Bayesian data mining algorithm was used for predicting the risk level and probability of heart disease. The system uses medical parameters to predict heart disease in patients and these parameters are age, sex, blood pressure, blood sugar (mg/dl), cholesterol (mm/dl),heart rate, exercise-induced angina, resting electrocardiogram, oldpeak, ST-slope and chest pain type. The data set collected by the system went through preprocessing which later supervised learning techniques and prediction model was conducted. Results were produced. Based on the results with the prediction accuracy of 90.97%, our results are in the same range as generated by other algorithms like KNN, Random Forest and Decision Tree algorithm.

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Section: Figure 1: Dataset Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assisted Artificial Intelligence Medical Diagnosis System for Heart Disease

Maambo¹,

Phiri²,

Kalumbilo³

et al. 2022

zictjournal

View full text Add to dashboard Cite

show abstract

“…applied Artificial Neural Networks for predicting heart diseases in people [6]. N.Al-milli used back-propagation neural network for prediction of heart disease with a good success rate [7].…”

Section: Related Workmentioning

confidence: 99%

Heart Disease Prediction Using Machine Learning Model Ensemble-Random Forest with Simple Regression

K¹

2020

IJATCSE

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Heterogeneous Criterion based Decision-making is the need of the hour when dealing with clinical data analysis such as Heart Disease data. A Machine Learning (ML) based model can effectively perform complex analysis and then predict the presence or absence of heart disease in an individual accurately. This analysis will aid the doctors in their diagnosis and save countless human lives. This paper proposes a hybrid model Random Forest with Simple Linear Regression (RFSLR) for classifying individuals as healthy individual or Heart Disease individual. The experimental analysis done by this work shows that RFSLR outperforms the existing classifiers Linear Regression(LR), Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB) and DTs(DT) in terms of performance metrics. This paper also recognizes the best features from the Cleveland Heart Disease Dataset (CHDD) using three Feature Selection Algorithm (FSA) RELIEF-F, LASSO and FOCUS.

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“…Their evaluation becomes very important. We generate results using a Artificial Neural Network ANN, which produces good performance in the prediction of heart disease [6], [18]. Neural network methods are introduced, which combine not only posterior probabilities but also predicted values from multiple predecessor techniques.…”

Section: Introductionmentioning

confidence: 99%

Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques

2019

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Heart disease is one of the most significant causes of mortality in the world today. Prediction of cardiovascular disease is a critical challenge in the area of clinical data analysis. Machine learning (ML) has been shown to be effective in assisting in making decisions and predictions from the large quantity of data produced by the healthcare industry. We have also seen ML techniques being used in recent developments in different areas of the Internet of Things (IoT). Various studies give only a glimpse into predicting heart disease with ML techniques. In this paper, we propose 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. The prediction model is introduced with different combinations of features and several known classification techniques. We produce an enhanced performance level with an accuracy level of 88.7% through the prediction model for heart disease with the hybrid random forest with a linear model (HRFLM).

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A Computational Intelligence Method for Effective Diagnosis of Heart Disease using Genetic Algorithm

Cited by 20 publications

References 17 publications

Assisted Artificial Intelligence Medical Diagnosis System for Heart Disease

Assisted Artificial Intelligence Medical Diagnosis System for Heart Disease

Heart Disease Prediction Using Machine Learning Model Ensemble-Random Forest with Simple Regression

Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques

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