Chronic kidney disease: a prediction and comparison of ensemble and basic classifiers performance

Chaurasia, Vikas; Pandey, Mithilesh Kumar; Pal, Saurabh

doi:10.1007/s42454-022-00040-y

Cited by 12 publications

(5 citation statements)

References 22 publications

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“…In the classification tasks, the best result in terms of accuracy was achieved by the decision forest method. Generally, ensemble methods (to which random forest belongs) yield better results than individual methods, which is confirmed by many relevant studies (e.g., Chaurasia et al, 2022;Dželihodžić & Đonko, 2016).…”

Section: Discussion Of Data Mining Methodsmentioning

confidence: 57%

Use of Data Mining for Analysis of Czech Real Estate Market

Tsakunov,

Chudán

2023

AIP

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This paper analyses data from the real estate market domain. The data were scraped from the bezrealitky.cz portal. The analysis looks at both sales and rental data. A total of 3546 records and 54 attributes were obtained. A basic overview of the data was performed using exploratory data analysis where some basic characteristics of the data were identified, such as the average price of sold and rented flats. More specific results were obtained by applying data mining methods such as regression (linear regression, lasso regression and ridge regression) for predicting the flat prices and payments for utilities, classification (support vector machines, KNN, Gaussian naïve Bayes, decision tree and random forest) for estimating the PENB class (building energy performance certificate) and building condition. Lasso regression performed the most successfully (R 2 = 0.76) in predicting the rent price. Among the classification tasks, the best result was achieved with random forest, which had an accuracy over 80% in some cases. Other tasks included clustering (k-means and k-modes) and anomaly detection (isolation forest). The main focus was on descriptive data mining, especially on clustering. Clusters created using the k-means algorithm (silhouette score of 0.78) with flats based on geographic coordinates were identified which show that the most expensive flats are on average in Bohemian regions, followed by Silesia and the cheapest are in central Moravia. Another cluster application identified flats in the Moravian-Silesian region with very high payments for utilities (silhouette score of 0.56). The models can help estimate the value of flats based on their attributes as well as location.

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Section: Discussion Of Data Mining Methodsmentioning

confidence: 57%

Use of Data Mining for Analysis of Czech Real Estate Market

Tsakunov,

Chudán

2023

AIP

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“…First, we discuss results based on all features, such as (f1, f2....f30), as shown in Table 3. The accuracy, recall, precision, f1-score, and confusion matrices, as well as the correlation matrix of features and ROC curve analysis, formed the basis for these actual results [47]. '…”

Section: Resultsmentioning

confidence: 99%

Efficient And Accurate Technique For Improving ML Classifier Performance Using Feature Selection In Phishing Website Prediction

Awasthi,

Goel

2024

eatp

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“…The following benchmark disease datasets were used for comparison with the APD dataset: Wisconsin Breast Cancer (WBC) [20][21][22], cardiovascular disease (CVD) [23][24][25][26][27], Pima Indians Diabetes Mellitus (PIMA) [28][29][30], chronic kidney disease (CKD) [31][32][33][34] and RA dataset [35].…”

Section: Assessment Of Relevant Imputation Techniques For the Apd Dat...mentioning

confidence: 99%

A Work Review on Clinical Laboratory Data Utilizing Machine Learning Use-Case Methodology

Ramasamy,

Santhoshkumar

2024

JIMH

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More than 140 autoimmune diseases have distinct autoantibodies and symptoms, and it makes it challenging to construct an appropriate model using Machine Learning (ML) for autoimmune disease. Arthritis-related autoimmunity requires special attention. Although many conventional biomarkers for arthritis have been established, more biomarkers of arthritis autoimmune diseases remain to be identified. This review focuses on the research conducted using data obtained from clinical laboratory testing of real-time arthritis patients. The collected data is labelled the Arthritis Profile Data (APD) dataset. The APD dataset is the retrospective data with many missing values. We undertook a comprehensive APD dataset study comprising four key steps. Initially, we identified suitable imputation techniques for the APD dataset. Subsequently, we conducted a comparative analysis with different benchmark disease datasets. We determined the most effective ML model for the APD dataset. Finally, identified the hidden biomarkers in the APD dataset. We applied various imputation techniques to handle these missing data on the APD dataset, and the best imputation techniques were determined using the degree of proximity (DoP) and degree of residual (DoR) procedure. The random value imputer and mode imputer are the suitable imputation techniques identified. Different benchmark disease datasets were compared using different hold-out (HO) methods and cross-validation (CV) folds, which highlights that the dataset properties significantly impact the performance of ML models. Random Forest (RndF) and XGBoost (XGB) are the best performing ML algorithms for most diseases, with accuracy consistently above 80%. The appropriate ML model for the APD dataset is the XGB (Extreme Gradient Boosting). Moreover, using the XGB feature importance concept significant features were identified for the APD dataset. The substantial and hidden biomarkers identified were Erythrocyte Sedimentation Rate (ESR), Antistreptolysin O (ASO), C-Reactive Protein (CRP), Rheumatoid Factor (RF), Lymphocytes (L), Absolute Eosinophil count (Abs), Uric_Acid, Red Blood Cell count (RBC), and Blood for Total Count (TC).

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Chronic kidney disease: a prediction and comparison of ensemble and basic classifiers performance

Cited by 12 publications

References 22 publications

Use of Data Mining for Analysis of Czech Real Estate Market

Use of Data Mining for Analysis of Czech Real Estate Market

Efficient And Accurate Technique For Improving ML Classifier Performance Using Feature Selection In Phishing Website Prediction

A Work Review on Clinical Laboratory Data Utilizing Machine Learning Use-Case Methodology

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