Review of Medical Disease Symptoms Prediction Using Data Mining Technique

Sah, Rahul Deo; Sheetalani, Jitendra

doi:10.9790/0661-1903015970

Cited by 16 publications

(2 citation statements)

References 17 publications

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“…Simons et al [ 9 ] used some predictive models like decision tables and neural networks for heart disorder data and it enabled them to predict Framingham risks in the heart for elderly people in Australia. Sah and Sheetalani [ 10 ] demonstrated the implementation of important predictive methods like nearest neighbors and support-vector machines to accurately predict cancer, liver, and heart risks from digital datasets. Patil et al [ 11 ] deployed a computational analytics framework for the prediction of diabetes disease symptoms using clustering techniques, which was followed by decision tree classifiers.…”

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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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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“…Some review papers available in the data mining and machine learning field provide a fairly good discussion of the classifiers that have been extensively applied for diagnosing a diverse spectrum of diseases [2], [29]. In the context of the disease diagnosis domain, the review papers incorporate diagnosis of diseases concerning cancer, appendicitis, cardio-vascular, skin, liver, thyroid, fertility, brain tumours, Parkinson's disease, hepatitis and many more [8], [30]. Recently, a widespread investigation of 32 classification approaches has been implemented to identify the most suitable method for disease diagnosis [7].…”

Section: The Related Workmentioning

confidence: 99%

Multi-Objective Hyperparameter Tuning of Classifiers for Disease Diagnosis

Kumar¹,

Ratnoo²

2021

INDJCSE

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Disease diagnosis is an arduous task due to the high cost associated with the misclassifications. The classifiers for disease diagnosis must be accurate, offer a tradeoff among sensitivity and specificity, and address the problem of class imbalance. Further, the ad-hoc hyperparameter settings deteriorate classifiers performance, specifically for disease diagnosis. For disease diagnosis systems, it is essential to address the issues of the setting of hyperparameters and class imbalance simultaneously. The objective of this paper is to propose a Multi-objective Hyperparameter Tuning approach, called MOHPT, in combination with resampling techniques. So far, the hyperparameters of classifiers for disease diagnosis have been often optimized based on a single objective criterion, i.e., accuracy. Multi-objective hyperparameter tuning in combination with resampling techniques is expected to enhance the efficacy of disease diagnosis. We have used decision trees (CART) and random forest as the classifiers. The proposed system MOHPT obtains non-dominated optimal hyperparameter configurations of these classifiers using crowding based sorting technique from multi-objective NSGA-II. The MOHPT uses sensitivity (true positive rate) and specificity (true negative rate) as the two objective functions for optimization, and each configuration in the non-dominated front of hyperparameter configurations belongs to a different tradeoff between sensitivity and specificity. The suggested approach offers a choice to medical practitioners to prefer any of the hyperparameter configurations. However, we have selected the hyperparameter configuration based on the optimum g-mean, a product of sensitivity and specificity. The resampling techniques such as undersampling, oversampling, SMOTE, RWO, MWSMOTE, and ROSE have been implemented to tackle the class imbalance. The MOHPT is tested on 17 medical datasets. Several combinations of decision tree and random forest classifiers have been implemented in combination with hyperparameter tuning and resampling techniques. The results have been validated using appropriate statistical tests. The result shows that the application of hyperparameter tuning in combination with sampling techniques significantly enhances the performance of disease diagnosis. Overall, the performance of the random forest is superior to the other classifiers. The MOHPT comparison with the related works further proves its efficacy. The suggested technique has been successful in finding the set of non-dominated set of solutions for hyperparameter configurations along with addressing the class imbalance problem. The results show a performance improvement on several performance evaluation metrics such as sensitivity, specificity, AUC etc.

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