An Efficient Framework for Prediction in Healthcare Data Using Soft Computing Techniques

Bhat, Veena H.; Rao, Prasanth G.; Krishna, S.; Shenoy, P. Deepa; Venugopal, K R; Patnaik, L. M.

doi:10.1007/978-3-642-22720-2_55

Cited by 12 publications

(8 citation statements)

References 5 publications

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“…Devised different predictive models have the potential to improve the chances for healing of patients, use different kinds of patient information and output prognostic results in a clinical setting [2]. According to [1] "This could be used for clinical decision support, disease surveillance, and population health management to improve patient care"…”

Section: Entrenova 12-14 September 2019mentioning

confidence: 99%

“…"Diabetes is a very serious disease that can lead to a large number of very serious long-term complications such as blindness, amputation and heart disease if not treated properly in time" [5] [6] [7]. Also, the very early stages of type 2 diabetes are asymptomatic, and therefore many patients can be undiagnosed for years [2]. "Treatment, especially with insulin, is not without adverse effects such as risk of hypoglycaemia and weight gain" [4].…”

Section: Entrenova 12-14 September 2019mentioning

confidence: 99%

“…Predictive Analytics involves different statistical models as well as analytical techniques that are used for devising different models that can predict many future occurrences [2].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Predictive analytics which is help to healthcare organizations to analyse data collected on the past and predict the possibilities of future occurrences in order to enable proper decisions of their patient [2]. Devising such predictive model can help healthcare to understand better and bring decisions based on informed data and with this to become more effective and efficient in solving health issues.…”

Section: Introductionmentioning

confidence: 99%

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Analyses of Diabetes Data and its Data Analytics Perspectives of Usage in the Health System in Kosovo

et al. 2019

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Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence.

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Section: Entrenova 12-14 September 2019mentioning

confidence: 99%

Section: Entrenova 12-14 September 2019mentioning

confidence: 99%

“…Predictive Analytics involves different statistical models as well as analytical techniques that are used for devising different models that can predict many future occurrences [2].…”

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analyses of Diabetes Data and its Data Analytics Perspectives of Usage in the Health System in Kosovo

et al. 2019

View full text Add to dashboard Cite

show abstract

“…V et al, [8], proposed a predictive model that emphasizes on a pre-processing phase. The model is shown to be the superior over existing techniques.…”

Section: Litreature Reviewmentioning

confidence: 99%

Predictive Analytic Modeling: A Walkthrough

Shah¹,

Butt

Baba

2017

IJARCSSE

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Abstract-Predictive analytic modeling is the key step involved in building a successful Data Mining solution. Predictive analytic modeling is a set of iterative activities in which a predictive model is built from the observed data set. Predictive analytics is the area of data mining concerned with the prediction of future probabilities and trends using archive data. It brings together management, information technology and modeling that eases researchers in making suitable predictions. Many methodologies have been proposed for model building, and these are based on some basic industrial engineering frameworks. The proposed research work discusses CRISP, DMAIC and SEMMA methodologies which provide the building blocks for an efficient predictive model. Keywords-Predictive Analytics, Data Mining, Modeling, CRISP, DMAIC and SEMMA. I. INTRODUCTIONToday's demanding atmosphere is forcing more organizations to explore and adopt predictive analytics. Data being the indispensible assets of an organization is increasing exponentially and so does the data storage need. Selling large amount of data has little value, but if one can add insight via analytics to data then we have the opportunity to monetize it. There is a sea change in the corporate world, researchers and IT organizations can seize upon the opportunity to reinvent themselves and transform from a support function into a profit center [1]. "We are sitting on a mountain of gold but we're not mining it as effectively as we could," says Michael Masciandaro, director of business intelligence at Rohm & Haas, a global specialty materials manufacturer. Data Mining is the technique that retrieves useful information from large amounts of data. Data mining is defined as a process involving the extraction of useful and interesting information from the underlying data [2]. Modeling is the main part of Predictive Analytics. Predictive Analytics modeling is used in representation of real world situations for rendering or description of reality. Limited, imprecise, but useful, a model helps us to make sense of the world [3]. The result of the models may give important information that can be used in decision making and future prediction. Predictive Analytic Modeling helps in optimizing existing processes, better understanding and predicting customer behavior, identify unexpected opportunities and recessions, and predict problems before they happen. The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting them to predict the unknown outcome. It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions [4]. Predictive analytic modeling involves searching for meaningful relationships among variables and representing those relationships in models. These variables are called predictors and constitute the core element of predictive analytics. A predictor could be measured for an indi...

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Comparing Statistical and Machine Learning Imputation Techniques in Breast Cancer Classification

Chlioui

Abnane

Idri

2020

Lecture Notes in Computer Science

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Missing data imputation is an important task when dealing with crucial data that cannot be discarded such as medical data. This study evaluates and compares the impacts of two statistical and two machine learning imputation techniques when classifying breast cancer patients, using several evaluation metrics. Mean, Expectation-Maximization (EM), Support Vector Regression (SVR) and K-Nearest Neighbor (KNN) were applied to impute 18% of missing data missed completely at random in the two Wisconsin datasets. Thereafter, we empirically evaluated these four imputation techniques when using five classifiers: decision tree (C4.5), Case Based Reasoning (CBR), Random Forest (RF), Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP). In total, 1380 experiments were conducted and the findings confirmed that classification using imputation based machine learning outperformed classification using statistical imputation. Moreover, our experiment showed that SVR was the best imputation method for breast cancer classification.

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An Efficient Framework for Prediction in Healthcare Data Using Soft Computing Techniques

Cited by 12 publications

References 5 publications

Analyses of Diabetes Data and its Data Analytics Perspectives of Usage in the Health System in Kosovo

Analyses of Diabetes Data and its Data Analytics Perspectives of Usage in the Health System in Kosovo

Predictive Analytic Modeling: A Walkthrough

Comparing Statistical and Machine Learning Imputation Techniques in Breast Cancer Classification

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