Multiple disease prediction using Machine learning algorithms

Arumugam, K.; Naved, Mohd; Shinde, Priyanka P.; Leiva-Chauca, Orlando; Huamán-Osorio, Antonio; Gonzales-Yanac, Tatiana

doi:10.1016/j.matpr.2021.07.361

Cited by 85 publications

(27 citation statements)

References 9 publications

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“…[30] finds that SVM performs better, averaging 96 percent accuracy. The DT model, according to the author in [31], consistently outperforms the NB and SVM models. According to its findings, SVM achieves an accuracy of 87%, DT achieves an accuracy of 90%, and LR achieves the highest accuracy in heart disease prediction when compared to DT, NB, SVM, and KNN, as shown in [32].…”

Section: Literature Reviewmentioning

confidence: 97%

A Machine Learning in Binary and Multiclassification Results on Imbalanced Heart Disease Data Stream

et al. 2022

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In medical filed, predicting the occurrence of heart diseases is a significant piece of work. Millions of healthcare-related complexities that have remained unsolved up until now can be greatly simplified with the help of machine learning. The proposed study is concerned with the cardiac disease diagnosis decision support system. An OpenML repository data stream with 1 million instances of heart disease and 14 features is used for this study. After applying to preprocess and feature engineering techniques, machine learning approaches like random forest, decision trees, gradient boosted trees, linear support vector classifier, logistic regression, one-vs-rest, and multilayer perceptron are used to perform binary and multiclassification on the data stream. When combined with the Max Abs Scaler technique, the multilayer perceptron performed satisfactorily in both binary (Accuracy 94.8%) and multiclassification (accuracy 88.2%). Compared to the other binary classification algorithms, the GBT delivered the right outcome (accuracy of 95.8%). Multilayer perceptrons, however, did well in multiple classifications. Techniques such as oversampling and undersampling have a negative impact on disease prediction. Machine learning methods like multilayer perceptrons and ensembles can be helpful for diagnosing cardiac conditions. For this kind of unbalanced data stream, sampling techniques like oversampling and undersampling are not practical.

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Section: Literature Reviewmentioning

confidence: 97%

A Machine Learning in Binary and Multiclassification Results on Imbalanced Heart Disease Data Stream

et al. 2022

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“…Different analytic methods like regression analysis, nearest neighbor, and decision tree were used for classification. Authors in [ 35 ] presented a predictive model to forecast various chronic risks using several machine learning methods like decision tree, ensemble classifiers, and probabilistic learners. A data mining-based disease recommendation system was developed in [ 36 ] that utilized online healthcare data records.…”

Section: Related Workmentioning

confidence: 99%

A Decisive Metaheuristic Attribute Selector Enabled Combined Unsupervised-Supervised Model for Chronic Disease Risk Assessment

Mishra

Thakkar

Singh

et al. 2022

Computational Intelligence and Neuroscience

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Advanced predictive analytics coupled with an effective attribute selection method plays a pivotal role in the precise assessment of chronic disorder risks in patients. Traditional attribute selection approaches suffer from premature convergence, high complexity, and computational cost. On the contrary, heuristic-based optimization to supervised methods minimizes the computational cost by eliminating outlier attributes. In this study, a novel buffer-enabled heuristic, a memory-based metaheuristic attribute selection (MMAS) model, is proposed, which performs a local neighborhood search for optimizing chronic disorders data. It is further filtered with unsupervised K-means clustering to remove outliers. The resultant data are input to the Naive Bayes classifier to determine chronic disease risks’ presence. Heart disease, breast cancer, diabetes, and hepatitis are the datasets used in the research. Upon implementation of the model, a mean accuracy of 94.5% using MMAS was recorded and it dropped to 93.5% if clustering was not used. The average precision, recall, and F-score metric computed were 96.05%, 94.07%, and 95.06%, respectively. The model also has a least latency of 0.8 sec. Thus, it is demonstrated that chronic disease diagnosis can be significantly improved by heuristic-based attribute selection coupled with clustering followed by classification. It can be used to develop a decision support system to assist medical experts in the effective analysis of chronic diseases in a cost-effective manner.

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“…However, no feature selection criteria were adopted for the presented study. In 2021, (Arumugam et al, 2021) presented a study on the utilization of machine learning for multiple disease prediction. The authors explored various prediction algorithms without performing any data analysis.…”

Section: Related Workmentioning

confidence: 99%

An optimal multi-disease prediction framework using hybrid machine learning techniques

Gupta¹,

Singh²

2022

KJS publishes peer-review articles in Mathematics, Computer Sci

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The prediction of lifestyle diseases is a vital domain in healthcare informatics research. This task is primarily achieved using the widely available machine learning algorithms. However, the highdimensionality of data amplifies the computation complexity and significantly reduces the models’ efficiency. Conspicuously, we presented a multi-disease prediction strategy for intelligent decision support using ensemble learning. The proposed work leverages genetic algorithm-based recursive feature elimination and AdaBoost to predict two prominent lifestyle diseases. Alongside the proposed approach, various benchmark machine learning techniques are also trained and validated using selected features under k-fold cross-validation. The results reveal the effectiveness of the proposed methodology in predicting multiple diseases in comparison to past works.

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Multiple disease prediction using Machine learning algorithms

Cited by 85 publications

References 9 publications

A Machine Learning in Binary and Multiclassification Results on Imbalanced Heart Disease Data Stream

A Machine Learning in Binary and Multiclassification Results on Imbalanced Heart Disease Data Stream

A Decisive Metaheuristic Attribute Selector Enabled Combined Unsupervised-Supervised Model for Chronic Disease Risk Assessment

An optimal multi-disease prediction framework using hybrid machine learning techniques

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