Dealing with Missing Values in Data

Kaiser, Jiří

doi:10.20470/jsi.v5i1.178

Cited by 89 publications

(41 citation statements)

References 2 publications

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“…Another way to improve the accuracy of the calculations would be to find out how to extract information from incomplete white to white measurements as this value is referred to as the third most important in predicting ELP (Mahdavi & Holladay, 2011). It is possible to determine how to handle missing values in datasets in order to maximize the information gain (Kaiser, 2014).…”

Section: Discussionmentioning

confidence: 99%

Improving clinical refractive results of cataract surgery by machine learning

Sramka

Slovak

Tuckova

et al. 2019

PeerJ

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Aim To evaluate the potential of the Support Vector Machine Regression model (SVM-RM) and Multilayer Neural Network Ensemble model (MLNN-EM) to improve the intraocular lens (IOL) power calculation for clinical workflow. Background Current IOL power calculation methods are limited in their accuracy with the possibility of decreased accuracy especially in eyes with an unusual ocular dimension. In case of an improperly calculated power of the IOL in cataract or refractive lens replacement surgery there is a risk of re-operation or further refractive correction. This may create potential complications and discomfort for the patient. Methods A dataset containing information about 2,194 eyes was obtained using data mining process from the Electronic Health Record (EHR) system database of the Gemini Eye Clinic. The dataset was optimized and split into the selection set (used in the design for models and training), and the verification set (used in the evaluation). The set of mean prediction errors (PEs) and the distribution of predicted refractive errors were evaluated for both models and clinical results (CR). Results Both models performed significantly better for the majority of the evaluated parameters compared with the CR. There was no significant difference between both evaluated models. In the ±0.50 D PE category both SVM-RM and MLNN-EM were slightly better than the Barrett Universal II formula, which is often presented as the most accurate calculation formula. Conclusion In comparison to the current clinical method, both SVM-RM and MLNN-EM have achieved significantly better results in IOL calculations and therefore have a strong potential to improve clinical cataract refractive outcomes.

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Section: Discussionmentioning

confidence: 99%

Improving clinical refractive results of cataract surgery by machine learning

Sramka

Slovak

Tuckova

et al. 2019

PeerJ

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show abstract

“…One of the other ways how to improve the accuracy of calculations would be to find a way how to extract information from incomplete WTW measurements as this value is referred as the third most important in predicting ELP (Mahdavi & Holladay, 2011). It is possible to find a way how to handle missing values in datasets in order to maximize information gain (Kaiser, 2014).…”

Section: Discussionmentioning

confidence: 99%

Peer Review #1 of "Improving clinical refractive results of cataract surgery by machine learning (v0.1)"

2019

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To evaluate potential of Support Vector Machine Regression model (SVM-RM) and Multilayer Neural Network Ensemble model (MLNN-EM) as an intraocular lens (IOL) power calculation improvement for clinical workflow. Background. Current IOL power calculation methods offer limited accuracy and especially in eyes with an unusual ocular dimension the accuracy may decrease. In case of an improperly calculated power of the IOL in cataract or refractive lens replacement surgery, there is a risk of re-operation or further refractive correction. This may potentially induce complications and discomfort to the patient. Methods. A dataset containing information about 2194 eyes was obtained using data mining process from Electronic Health Record (EHR) system database of Gemini Eye Clinic. The dataset was optimized and split into Selection set (used in design of models and training), and Verification set (used in the evaluation). Set of mean prediction errors and distribution of predicted refractive error were evaluated for both models and Clinical results. Results. Both models performed significantly better for the most evaluated parameters compared to the clinical results. There was no significant difference between both evaluated models. In the ± 0.50 D prediction error category both SVM-RM and MLNN-EM were slightly better than Barrett Universal II formula (which is often presented as the most accurate calculation formula). Conclusion. In comparison to the method currently used in a clinical setting, both SVM-RM and MLNN-EM has achieved significantly better results in IOL calculations and therefore there is a strong potential to improve clinical cataract refractive outcomes.

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“…With respect to diabetic prediction, we have applied various supervised machine learning algorithms as the dataset of Pima is labeled. The classifiers cannot correctly classify, due to the presence of missing values and outliers present [7][8][9][10][11]. In mathematics, outlier and missing value handling is an important issue which cannot be ignored.…”

Section: Introductionmentioning

confidence: 99%

A Diabetic Disease Prediction Model Based on Classification Algorithms

Ahuja¹,

Sharma²,

Ali³

2019

AETiC

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Diabetes is one of the chronic diseases in the world, 246 million people are inflicted by this disease and according to a World Health Organisation (WHO) report, this figure will increase to 380 million sufferers by 2025. Many other debilitating and critical health issues may further develop if this disease is not diagnosed or remain unidentified. Machine Learning (ML) techniques are now being used in various fields like education, healthcare, business, recommendation system, etc. Healthcare data is complex and high in dimensionality and contains irrelevant information - due to this, the prediction accuracy is low. The Pima Indians Diabetes Dataset was used in this research, it consisted of 768 records. Firstly, the missing values are replaced by the median followed by Linear Discriminant Analysis. Using the Python programming language, feature selection techniques is applied in combination with five classification algorithms: Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Logistic Regression, Random Forest and Decision Tree. The aim of this paper is to compare the different classification algorithms in order to predict diabetes in patients more accurately. K-fold cross-validation is applied, considering k to be 2, 4, 5 and 10. The performance parameters taken are the: accuracy, precision, recall, F Score and area under the curve. Our study found that the MLP classifier gave the highest accuracy of 78.7% with a recall of 61.26%, precision of 72.45% and F1 Score of 65.97% for k = 4.

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Dealing with Missing Values in Data

Cited by 89 publications

References 2 publications

Improving clinical refractive results of cataract surgery by machine learning

Improving clinical refractive results of cataract surgery by machine learning

Peer Review #1 of "Improving clinical refractive results of cataract surgery by machine learning (v0.1)"

A Diabetic Disease Prediction Model Based on Classification Algorithms

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