A missing data imputation approach using clustering and maximum likelihood estimation

Albayrak, Muammer; Turhan, Kemal; Kurt, Burçin

doi:10.1109/tiptekno.2017.8238064

Cited by 8 publications

(3 citation statements)

References 1 publication

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“…Missing value is a problem related to replacing null values in data variables [41]. Missing values in data can be caused by human error when entering data [42], the patient did not provide information [43], and patient data security [44]. In general, missing value imputation techniques can be grouped into two types: statistic-based techniques and machine learning [45]-- [47].…”

Section: ) Missing Valuesmentioning

confidence: 99%

Chronic Diseases Prediction Using Machine Learning With Data Preprocessing Handling: A Critical Review

Ghaniaviyanto Ramadhan,

Adiwijaya,

Maharani

et al. 2024

IEEE Access

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According to the World Health Organization (WHO), some chronic diseases such as diabetes mellitus, stroke, cancer, cardiac vascular, kidney failure, and hypertension are essential for early prevention. One of the prevention that can be taken is to predict chronic diseases using machine learning based on personal medical record or general checkup result. The common prediction objective is to minimize the prediction error as low as possible. The most influencing chronic diseases prediction factors are the quality of data and the choice of predictor such as machine learning methods. The five main problems those lower data quality are outliers, missing values, feature selection, normalization, and imbalance. After we ensure the quality of data, the next task is to choose the best machine learning methods. The most influencing factor to consider when we choose the predictor its performance evaluation (accuracy, recall, precision, f1-score). Thus, predicting chronic disease aims to produce increased performance and solve problems in medical data. This paper presents a Systematic Literature Review (SLR) that offers a comprehensive discussion of research on chronic diseases prediction using machine learning and its data preprocessing handling. This paper covers machine learning methods discussion such as supervised learning, ensemble learning, deep learning, and reinforcement learning. The preprocessing handling we discuss includes missing values, outliers, feature selection, normalization, and imbalance. The final discussions of this paper are open issues, and the potential future works in improving the prediction performance for chronic diseases using a data preprocessing handling and machine learning methods.

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Section: ) Missing Valuesmentioning

confidence: 99%

Chronic Diseases Prediction Using Machine Learning With Data Preprocessing Handling: A Critical Review

Ghaniaviyanto Ramadhan,

Adiwijaya,

Maharani

et al. 2024

IEEE Access

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“…The success of the method to fill the missing data is affected by many factors such as the characteristics of the data and the type of missing data. 28 For this reason, choosing the appropriate method to fill the missing data in datasets is an important and necessary preliminary process in machine learning studies.…”

Section: Filling Missing Datamentioning

confidence: 99%

Analyzing the effect of data preprocessing techniques using machine learning algorithms on the diagnosis of COVID‐19

Erol

Uzbaş

Yücelbaş

et al. 2022

Concurrency and Computation

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Real-time polymerase chain reaction (RT-PCR) known as the swab test is a diagnostic test that can diagnose COVID-19 disease through respiratory samples in the laboratory. Due to the rapid spread of the coronavirus around the world, the RT-PCR test has become insufficient to get fast results. For this reason, the need for diagnostic methods to fill this gap has arisen and machine learning studies have started in this area.On the other hand, studying medical data is a challenging area because the data it contains is inconsistent, incomplete, difficult to scale, and very large. Additionally, some poor clinical decisions, irrelevant parameters, and limited medical data adversely affect the accuracy of studies performed. Therefore, considering the availability of datasets containing COVID-19 blood parameters, which are less in number than other medical datasets today, it is aimed to improve these existing datasets. In this direction, to obtain more consistent results in COVID-19 machine learning studies, the effect of data preprocessing techniques on the classification of COVID-19 data was investigated in this study. In this study primarily, encoding categorical feature and feature scaling processes were applied to the dataset with 15 features that contain blood data of 279 patients, including gender and age information. Then, the missingness of the dataset was eliminated by using both K-nearest neighbor algorithm (KNN) and chain equations multiple value assignment (MICE) methods. Data balancing has been done with synthetic minority oversampling technique (SMOTE), which is a data balancing method. The effect of data preprocessing techniques on ensemble learning algorithms bagging, AdaBoost, random forest and on popular classifier algorithms KNN classifier, support vector machine, logistic regression, artificial neural network, and decision tree classifiers have been analyzed. The highest accuracies obtained with the bagging classifier were 83.42% and 83.74% with KNN and MICE imputations by applying SMOTE, respectively. On the other hand, the highest accuracy ratio reached with the same classifier without SMOTE was 83.91% for the KNN imputation. In conclusion, certain data preprocessing techniques are examined comparatively and the effect of these data preprocessing techniques on success is presented and the importance of the right combination of data preprocessing to achieve success has been demonstrated by experimental studies.

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“…The imputation error rate is low. M. Albayrak et al [14], presented a realistic comparison of multiple imputations. The recurrent association of data learning neural network models for estimating missing values.…”

Section: Related Workmentioning

confidence: 99%

Improving Classification Performance of Neuro-Fuzzy Classifier by Imputing Missing Data

Tarle¹,

Akkalaksmi²

2019

IJC

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In medical data classification, if the size of data sets is small and if it contains multiple missing attribute values, in such cases improving classification performance is an important issue. The foremost objective of machine learning research is to improve the classification performance of the classifiers. The number of training instances provided for training must be sufficient in size. In the proposed algorithm, we substitute missing attribute values with attribute available domain values and generate additional training tuples that are in addition to original training tuples. These additional, plus original training samples provide sufficient data samples for learning. The neuro-fuzzy classifier trained on this dataset. The classification performance on test data for the neuro-fuzzy classifier is obtained using the k-fold cross-validation method. The proposed method attains around 2.8% and 3.61% improvement in classification accuracy for this classifier.

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A missing data imputation approach using clustering and maximum likelihood estimation

Cited by 8 publications

References 1 publication

Chronic Diseases Prediction Using Machine Learning With Data Preprocessing Handling: A Critical Review

Chronic Diseases Prediction Using Machine Learning With Data Preprocessing Handling: A Critical Review

Analyzing the effect of data preprocessing techniques using machine learning algorithms on the diagnosis of COVID‐19

Improving Classification Performance of Neuro-Fuzzy Classifier by Imputing Missing Data

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