Survey of Machine Learning Algorithms for Disease Diagnostic

Fatima, Meherwar; Pasha, Maruf

doi:10.4236/jilsa.2017.91001

Cited by 479 publications

(309 citation statements)

References 7 publications

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“…There is little research on the classification of the heart disease dataset. Many of them show good classification accuracy [9].…”

Section: Related Workmentioning

confidence: 99%

Heart Disease Prediction and Classification Using Machine Learning Algorithms Optimized by Particle Swarm Optimization and Ant Colony Optimization

Khourdifi¹,

Bahaj²

2019

IJIES

211

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The prediction of heart disease is one of the areas where machine learning can be implemented. Optimization algorithms have the advantage of dealing with complex non-linear problems with a good flexibility and adaptability. In this paper, we exploited the Fast Correlation-Based Feature Selection (FCBF) method to filter redundant features in order to improve the quality of heart disease classification. Then, we perform a classification based on different classification algorithms such as K-Nearest Neighbour, Support Vector Machine, Naïve Bayes, Random Forest and a Multilayer Perception | Artificial Neural Network optimized by Particle Swarm Optimization (PSO) combined with Ant Colony Optimization (ACO) approaches. The proposed mixed approach is applied to heart disease dataset; the results demonstrate the efficacy and robustness of the proposed hybrid method in processing various types of data for heart disease classification. Therefore, this study examines the different machine learning algorithms and compares the results using different performance measures, i.e. accuracy, precision, recall, f1-score, etc. A maximum classification accuracy of 99.65% using the optimized model proposed by FCBF, PSO and ACO. The results show that the performance of the proposed system is superior to that of the classification technique presented above.

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“…There is little research on the classification of the heart disease dataset. Many of them show good classification accuracy [9].…”

Section: Related Workmentioning

confidence: 99%

Heart Disease Prediction and Classification Using Machine Learning Algorithms Optimized by Particle Swarm Optimization and Ant Colony Optimization

Khourdifi¹,

Bahaj²

2019

IJIES

211

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“…Machine learning (ML) techniques have already been applied for various diseases. 8 Previous studies have complemented administrative data with clinical information to develop predictive models. However, clinical data are not always integrated electronically in health databases in many jurisdictions, making it problematic to apply predictive models derived from clinical data.…”

Section: Introductionmentioning

confidence: 99%

Machine learning‐based prediction of heart failure readmission or death: implications of choosing the right model and the right metrics

et al. 2019

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Aims Machine learning (ML) is widely believed to be able to learn complex hidden interactions from the data and has the potential in predicting events such as heart failure (HF) readmission and death. Recent studies have revealed conflicting results likely due to failure to take into account the class imbalance problem commonly seen with medical data. We developed a new ML approach to predict 30 day HF readmission or death and compared the performance of this model with other commonly used prediction models. Methods and results We identified all Western Australian patients aged above 65 years admitted for HF between 2003 and 2008 in the linked Hospital Morbidity Data Collection. Taking into consideration the class imbalance problem, we developed a multi‐layer perceptron (MLP)‐based approach to predict 30 day HF readmission or death and compared the predictive performances using the performance metrics, that is, area under the receiver operating characteristic curve (AUC), area under the precision–recall curve (AUPRC), sensitivity and specificity with other ML and regression models. Out of the 10 757 patients with HF, 23.6% were readmitted or died within 30 days of hospital discharge. We observed an AUC of 0.55, 0.53, 0.58, and 0.54 while an AUPRC of 0.39, 0.38, 0.46, and 0.38 for weighted random forest, weighted decision trees, logistic regression, and weighted support vector machines models, respectively. The MLP‐based approach produced the highest AUC (0.62) and AUPRC (0.46) with 48% sensitivity and 70% specificity. Conclusions We show that for the medical data with class imbalance, the proposed MLP‐based approach is superior to other ML and regression techniques for the prediction of 30 day HF readmission or death.

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“…Kumar et al discussed the method provides to prevention control and awareness of diabetes and effect of relevant other diseases [8]. Fatima et al focused the Data Mining algorithms to gain its strength due to the managing capability of a large amount of data is to combined from several different sources and integrating the background information [9]. Priya and Karthikeyan have discussed different clustering algorithms and compared for outlier detection.…”

Section: Related Workmentioning

confidence: 99%

Data Mining Technique for Diabetes Diagnosis using Classification Algorithms

Priya*,

Karthikeyan

2019

IJRTE

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 Abstract: Diabetes mellitus is defined as a one of the chronic and deadliest diseases which combined with abnormally high level of sugar (glucose) in the blood. The classification technique helps in diagnosis the symptoms at starting stages. This paper focused to prognosticate the chance of diabetes in patients with extremely correct classification of Diabetes. The classification algorithms viz., Naïve Bayes, Logistic Regression, and Decision Tree can be used to detect diabetes at an early stage. The algorithm performances are evaluated based on various measures like Recall, Precision, and F-Measure. Experiments are conducted where the time complexity of each of the algorithm is measured. Accuracy is also measured over correct classification and misclassification instances, observed that a Logistic Regression algorithm has much better performance when compared to the other type classifications. Using Receiver OperatingCharacteristic curves the results are verified in a systematic manner.

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Survey of Machine Learning Algorithms for Disease Diagnostic

Cited by 479 publications

References 7 publications

Heart Disease Prediction and Classification Using Machine Learning Algorithms Optimized by Particle Swarm Optimization and Ant Colony Optimization

Heart Disease Prediction and Classification Using Machine Learning Algorithms Optimized by Particle Swarm Optimization and Ant Colony Optimization

Machine learning‐based prediction of heart failure readmission or death: implications of choosing the right model and the right metrics

Data Mining Technique for Diabetes Diagnosis using Classification Algorithms

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