Machine Learning-Based Missing Value Imputation Method for Clinical Datasets

Rahman, M. Mostafizur; Davis, Darryl N.

doi:10.1007/978-94-007-6190-2_19

Cited by 54 publications

(31 citation statements)

References 24 publications

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“…It has demonstrated to be a robust method, performing properly in a bunch of scenarios, e.g., the prediction of missing attribute values on real cardiovascular data was improved by the use of FURIA in [48]; FURIA turned out as a competitive alternative for recommender systems in [49]; fuzzy rule-based ensembles were constructed including FURIA in order to derive good performance for high dimensional problems [50,51]; etc.…”

Section: Analyzing Furia Inference Mechanism By Fingramsmentioning

confidence: 99%

Enhancing Fingrams to deal with precise fuzzy systems

Pancho

Alonso

Magdalena

2016

Fuzzy Sets and Systems

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Section: Analyzing Furia Inference Mechanism By Fingramsmentioning

confidence: 99%

Enhancing Fingrams to deal with precise fuzzy systems

Pancho

Alonso

Magdalena

2016

Fuzzy Sets and Systems

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“…Beberapa metode dalam data mining dapat digunakan untuk menangani data dengan missing value, diantaranya dengan tidak menggunakan/menghapus data dengan missing value dari dataset, dengan mengganti dengan nilai tertentu (imputasi), yaitu dengan menggunakan nilai rata-rata fiturnya, dengan nilai modulus (nilai yang sering muncul), atau dengan menggunakan pendekatan algoritma machine learning untuk memprediksi missing value, seperti k-NN, k-Means, dsb. Pada beberapa penelitian (Rahman 2013, Mehala 2009) disimpulkan bahwa proses imputasi dengan menggunakan algoritma machine learning memperoleh hasil yang lebih baik dibandingkan dengan menggunakan nilai ratarata ataupun modulus. Oleh sebab itu, pada penelitian ini digunakan algoritma k-NN untuk mengganti missing value yang ada pada dataset tsunami seperti tampak pada Gambar 5.…”

Section: Tsunamiunclassified

Clustering the Potential Risk of Tsunami Using Density-Based Spatial Clustering of Application With Noise (Dbscan)

Furqon

Muflikhah

2016

JEEST

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Tsunami is one of the deadliest natural disaster that causing devastating property damage and loss of life. Therefore, this triggers many scientist to do researches in tsunami mitigation disaster, such as analyzing the potential risks caused by tsunami. The process of analyzing the potential risk caused by tsunami can be done by grouping the data of tsunami based on characteristics of the previous tsunami events. DBSCAN (Density-based Spatial Clustering of Application with Noise) is a popular clustering method and can be used to do the task. The algorithm do the clustering processes using density-based concept that able to detect outlier/noise and clusters irregular shapes. It was proved in this research where the evaluation method using Silhouette Coefficient on the DBSCAN clustering result gave highest value 0.96056649 for ε and minPts value of .1 and 0.1.

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“…To achieve the best possible performance with a learning algorithm on a particular training set, a feature subset selection method considers the interaction between the algorithm and the training dataset. In [17] authors performed missing value handling on cardiovascular dataset using fuzzy unordered rule induction algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

An Ensemble approach on Missing Value Handling in Hepatitis Disease Dataset

Radhakrishnan¹,

Priyaa²

2015

IJCA

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The Major work in data pre-processing is handling Missing value imputation in Hepatitis Disease Diagnosis which is one of the primary stage in data mining. Many health datasets are typically imperfect. Just removing the cases from the original datasets can fetch added problems than elucidations. A appropriate technique for missing value imputation can assist to generate high-quality datasets for enhanced scrutinizing in clinical trials. This paper investigates the exploit of a machine learning technique as a missing value imputation process for incomplete Hepatitis data. Mean/mode imputation, ID3 algorithm imputation, decision tree imputation and proposed bootstrap aggregation based imputation are used as missing value imputation and the resultant datasets are classified using KNN. The experiment reveals that classifier performance is enhanced when the Bagging based imputation algorithm is used to foresee missing attribute values.

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Machine Learning-Based Missing Value Imputation Method for Clinical Datasets

Cited by 54 publications

References 24 publications

Enhancing Fingrams to deal with precise fuzzy systems

Enhancing Fingrams to deal with precise fuzzy systems

Clustering the Potential Risk of Tsunami Using Density-Based Spatial Clustering of Application With Noise (Dbscan)

An Ensemble approach on Missing Value Handling in Hepatitis Disease Dataset

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