<scp>RFFS</scp>: Recursive random forest feature selection based ensemble algorithm for chronic kidney disease prediction

Theerthagiri, Prasannavenkatesan; Ruby, A. Usha

doi:10.1111/exsy.13048

Cited by 11 publications

(3 citation statements)

References 32 publications

(52 reference statements)

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“…We used the Random Forest (RF) [50] algorithm to get the most important attributes. We decided to use this technique, given that existing literature [51][52][53] provides substantial evidence through comparative assessments that RF represents an effective data-driven approach for identifying relevant input features across various use cases.…”

Section: Most Important Attributesmentioning

confidence: 99%

Machine Learning Analysis of Factors Contributing to Diabetes Development

Edgar Ceh-Varela,

Maes,

Sarbagya Ratna Shakya

2024

Cloud Computing and Data Science

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Diabetes is a chronic condition that affects how the body processes blood sugar. Early diagnosis and management of diabetes are essential for preventing these complications. Machine Learning (ML) techniques offer an effective means to accurately diagnose diabetes by identifying key risk factors and developing predictive models. In this study, we assess the performance of 11 ML algorithms on four diabetes prediction datasets, considering the top 2, top 3, and all attributes. Through k-fold cross-validation, we ensure robust and generalizable results. We use a set of standard evaluation metrics such as accuracy, precision, recall, f1-score, and Receiver Operating Characteristic curve (ROC_AUC). Our analysis aims to determine the optimal number of features and assess how performance changes with feature additions. Notably, some ML classifiers achieve satisfactory classification and predictive abilities using only the top 2 or 3 features. Furthermore, varying dataset performances across algorithms highlight the need for assessing multiple models to identify the most suitable one. These findings enable the creation of dependable models that enhance patient outcomes by leveraging effective algorithms and pertinent features.

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Section: Most Important Attributesmentioning

confidence: 99%

Machine Learning Analysis of Factors Contributing to Diabetes Development

Edgar Ceh-Varela,

Maes,

Sarbagya Ratna Shakya

2024

Cloud Computing and Data Science

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“…CKD dataset with a large number of attributes was employed in this work, and the overfitting issue is typically a concern with big datasets. We employed an RF-based classification model, which considerably decreases the overfitting problems [15].…”

Section: Proposed ML Teachniques For Model Trainingmentioning

confidence: 99%

Enhancing Chronic Kidney Disease Diagnosis using Machine Learning Classifiers: A Comparative Analysis

2023

IJANSER

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One of the most common and dangerous illnesses affecting people on a global scale, chronic kidney disease (CKD), does not manifest itself until the kidneys of a particular person have sustained irreparable harm. The progression of CKD is linked to many serious side effects, such as an increased risk of different diseases, kidney failure, nerve harm, pregnancy problems, anemia, and hyperlipidemia. This illness claims the lives of millions of individuals each year. Since there are no significant symptoms that can be used as a benchmark to identify CKD, diagnosing the condition might be difficult. Occasionally, data may be interpreted wrongly when the diagnosis is persistent. To diagnose CKD in patients, this study employs a machine learning classifier. Six machine learning (ML) techniques are used in this study, including Random Forest (RF), Random Tree (RT), Decision Table (DTa), Decision Tree (DTr), Naïve Bayes (NB), and Hoeffding Tree and multiple performance metrics are considered such as accuracy, TPR, FPR, recall and mean absolute error (MAE). To select the most accurate classifier for predicting CKD, these predictive models are created using a dataset on chronic kidney disease containing 279 attributes acquired from Kaggle. Our objective is to ease the introduction of machine learning techniques for precisely detecting CKD by learning from dataset attribute reports. The main contribution of the research is an MLbased model for diagnosing chronic renal disease that outperforms common diagnosing techniques and reaches the highest predicted accuracy. This study also contrasted how well each model performed. We were able to predict this disease with the Random Forest model more accurately than ever before, at a 76.23% accuracy level.

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“…In this approach, each data point is represented as a point in an n-dimensional space (where n is the number of features), with each feature's value having a specific coordinate value. Then, classification is carried out by identifying the hyper-plane that effectively distinguishes the two classes [17]. This work selects the hyper-plane that better segregate the two classes.…”

Section: Algorithms and Techniques For Quality Estimationmentioning

confidence: 99%

An Investigation of Wine Quality Testing using Machine Learning Techniques

2020

jxu

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Quality is the most determining factor for any product. Optimal care and best measures are to be taken in assessing the quality of any product. This work deals with determining the quality of wine using intelligence-based learning techniques. In order to estimate the quality of wine, several experiments are performed on wine datasets. The main purpose of our work is to study and discover an efficient machine learning (ML) model that could determine the quality of wine given some Physico-chemical features. This study establishes that selecting important features to evaluate rather than all of them can lead to improved forecasts. According to the results, this approach may provide people who are not wine experts a greater opportunity to choose a fine wine.

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RFFS: Recursive random forest feature selection based ensemble algorithm for chronic kidney disease prediction

Cited by 11 publications

References 32 publications

Machine Learning Analysis of Factors Contributing to Diabetes Development

Machine Learning Analysis of Factors Contributing to Diabetes Development

Enhancing Chronic Kidney Disease Diagnosis using Machine Learning Classifiers: A Comparative Analysis

An Investigation of Wine Quality Testing using Machine Learning Techniques

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