Data Mining Techniques for Disease Risk Prediction Model: A Systematic Literature Review

Ahmad, Wan Muhamad Taufik Wan; Ghani, Nur Laila Ab; Drus, Sulfeeza Mohd

doi:10.1007/978-3-319-99007-1_4

Cited by 12 publications

(6 citation statements)

References 11 publications

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“…SVM learning algorithm is presented in Figure 6. SVM learning technique employs input vectors to map nonlinearly into a feature space whose dimension is high [52][53][54].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

“…SVM is a well-known ML technique which is widely used for both classification and regression analysis, due to its high accuracy [1,51,52]. SVM is defined as a statistical learning concept with an adaptive computational learning method.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

“…Decision Tree is a network system composed primarily of nodes and branches, and nodes comprising root nodes and intermediate nodes. The intermediate nodes are used to represent a feature, and the leaf nodes are used to represent a class label [52]. DT can be used for feature selection [57].…”

Section: Decision Tree (Dt)mentioning

confidence: 99%

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Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0

et al. 2020

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Recently, with the emergence of Industry 4.0 (I4.0), smart systems, machine learning (ML) within artificial intelligence (AI), predictive maintenance (PdM) approaches have been extensively applied in industries for handling the health status of industrial equipment. Due to digital transformation towards I4.0, information techniques, computerized control, and communication networks, it is possible to collect massive amounts of operational and processes conditions data generated form several pieces of equipment and harvest data for making an automated fault detection and diagnosis with the aim to minimize downtime and increase utilization rate of the components and increase their remaining useful lives. PdM is inevitable for sustainable smart manufacturing in I4.0. Machine learning (ML) techniques have emerged as a promising tool in PdM applications for smart manufacturing in I4.0, thus it has increased attraction of authors during recent years. This paper aims to provide a comprehensive review of the recent advancements of ML techniques widely applied to PdM for smart manufacturing in I4.0 by classifying the research according to the ML algorithms, ML category, machinery, and equipment used, device used in data acquisition, classification of data, size and type, and highlight the key contributions of the researchers, and thus offers guidelines and foundation for further research.

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“…SVM learning algorithm is presented in Figure 6. SVM learning technique employs input vectors to map nonlinearly into a feature space whose dimension is high [52][53][54].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

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Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0

et al. 2020

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“…A methodical review and creative writing about five databases initiate 170 articles, of which 7 items were nominated in the ending activity. This evaluation instituted that utmost prediction patterns used classification method, with an emphasis on decision tree, neural network, support vector machines, and Naïve Bayes algorithms everywhere heart-associated illness is usually researched [9]. This survey paper primarily reports on the optimization procedures of Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), and Ant Colony Optimization (ACO), that E-ISSN 2581 -7957 CVR Journal of Science and Technology, Volume 16, June 2019 P-ISSN 2277 -3916 DOI: 10.32377/cvrjst1616 remain exploited recently with Fuzzy Logic (FL) to recuperate the accomplishment of the optimization processes [10].…”

Section: Review Of Literaturementioning

confidence: 99%

Future Trends of the Healthcare Data Predictive Analytics using Soft Computing Techniques in Data Science

Dhamodaran¹,

Balmoor²

2019

CVRJST

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Predictive Analytics, Soft Computing (SC) and Optimization, Data Mining and Data Science are rapidly becoming some of the most-discussed, perhaps utmost glorified topics in healthcare business. Artificial Intelligence, Machine Learning, Artificial Neural Networks, Fuzzy Logic, Expert Systems, etc., is well-studied disciplines with a long history of success in many industries. Healthcare can acquire treasured sessions from this prior achievement to startup the efficacy of predictive analytics for refining patient care, chronic disease management, hospital administration and supply chain efficiencies. The prospects that presently occurs for healthcare systems is to state what "predictive analytics" stands for to them and how can it be cast off furthermost excellently to cause further enhancements.In all industries including healthcare, prediction plays a best worthwhile role when that data is passed on as accomplishments. The inclinations to mediate the vital data is in harnessing the power of historical and real-time data with visions from forecasting those data based on the times ahead. Importantly, to best gauge efficacy and value, both the predictor and the intervention must be integrated within the same system and workflow where the trend occurs.A valuable report of the organized publicity and expectation of predictive analytics in healthcare through a blend of psychology, digital technology, and entrepreneurship is available for real-time implementation for the good of the public. Review and evaluation on these disciplines pave ways to open up new arenas envisaging the future trends of Predictive analytics, Data Mining and Science and Soft Computing (SC) in healthcare, stepping strongly into pervasive computing, ambient intelligence, ubiquitous computing and many more automated technical concepts and computing's ahead.

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“…A number of studies related to machine learning modelling of infectious disease have been conducted on unsupervised analysis of several infectious diseases (PeterIdowu et al, 2013),measles outbreak prediction (Liao et al, 2017), dengue outbreak prediction (Rahmawati & Huang, 2016) and dengue infection risk (Fathima & Hundewale, 2012). Our prior work found that decision tree and Naive Bayes are among the techniques commonly used for disease risk prediction (Ahmad et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Handling Imbalanced Class Problem of Measles InfectionRisk Prediction Model

Ahmad

Ghani²,

Drus³

2019

IJEAT

Self Cite

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Measles is an emerging infectious disease with increasing number of reported cases. It is a vaccine-preventable disease;thus, it is common to have imbalanced class problem in the dataset. This study aims to resolve the imbalanced class problem for the prediction of measles infection risk and to compare the predictive results on a balanced dataset based on three machine learningtechniques. The data that was utilized in this study contained 37,884 records of suspected measles casesthat were highly imbalanced towards negative measles cases. The Synthetic Minority Over-Sampling Technique (SMOTE) was performed to balance thedistribution of the target attribute. The balanced dataset was then modelled using logistic regression, decision tree and Naïve Bayes. The predicted results indicated that logistic regression executed on the balanced dataset by SMOTE has the highest and most accurateclassification with 94.5% overall accuracy, 93.9% true positive rate, 5.8% false positive rate and 5.1% false negative rate. Therefore, SMOTE and other over-sampling approaches may be applicable to overcome imbalanced class issues in the medical dataset.

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Data Mining Techniques for Disease Risk Prediction Model: A Systematic Literature Review

Cited by 12 publications

References 11 publications

Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0

Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0

Future Trends of the Healthcare Data Predictive Analytics using Soft Computing Techniques in Data Science

Handling Imbalanced Class Problem of Measles InfectionRisk Prediction Model

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