Analysis of the performance of feature optimization techniques for the diagnosis of machine learning-based chronic kidney disease

Hossain, Muhammad Minoar; Swarna, Reshma Ahmed; Mostafiz, Rafid; Shaha, Pabon; Pinky, Lubna Yasmin; Rahman, Mohammad Motiur; Rahman, Wahidur; Hossain, Md. Selim; Hossain, Md. Elias; Iqbal, Md. Sadiq

doi:10.1016/j.mlwa.2022.100330

Cited by 9 publications

(8 citation statements)

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“…When compared to other techniques, the proposed DFS-ODBN Rady and Anwar [14] Rubini and Perumal [16] Elhoseny et al [18] Chittora et al [20] M.M. Hossain [22] Proposed model Rady and Anwar [14] Rubini and Perumal [16] Elhoseny et al [18] Chittora et al [20] M.M. Hossain [22] Proposed model…”

Section: Discussionmentioning

confidence: 99%

“…Hossain [22] Proposed model Rady and Anwar [14] Rubini and Perumal [16] Elhoseny et al [18] Chittora et al [20] M.M. Hossain [22] Proposed model…”

Section: Discussionmentioning

confidence: 99%

“…To properly pick the features subset, several feature optimization techniques were described to see the effect of them on the performance of the ML model which was tested on five influential classification models Logistic regression , Random forest (RF), SVM , K-nearest neighbors and Xtreme gradient boosting (XGB), experiments have shown that the accuracy of the model can be enhanced by using Linear discriminant analysis (LDA) feature optimizer that performs the highest outstanding result [22].…”

Section: Related Workmentioning

confidence: 99%

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Enhanced Optimized Classification Model of Chronic Kidney Disease

Elkholy¹,

Rezk²,

Saleh³

2023

IJACSA

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Chronic kidney disease (CKD) is one of the leading causes of death across the globe, affecting about 10% of the world's adult population. Kidney disease affects the proper function of the kidneys. As the number of people with chronic kidney disease (CKD) rises, it is becoming increasingly important to have accurate methods for detecting CKD at an early stage. Developing a mechanism for detecting chronic kidney disease is the study's main contribution to knowledge. In this study, preventive interventions for CKD can be explored using machine learning techniques (ML). The Optimized deep belief network (DBN) based on Grasshopper's Optimization Algorithm (GOA) classifier with prior Density-based Feature Selection (DFS) algorithm for chronic kidney disease is described in this study, which is called "DFS-ODBN." Prior to the DBN classifier, whose parameters are optimized using GOA, the proposed method eliminates redundant or irrelevant dimensions using DFS. The proposed DFS-ODBN framework consists of three phases, preprocessing, feature selection, and classification phases. Using CKD datasets, the suggested approach is also tested, and the performance is evaluated using several assessment metrics. Optimized-DBN achieves its maximum performance in terms of sensitivity, accuracy, and specificity, the proposed DFS-ODBN demonstrated accuracy of 99.75 percent using fewer features comparing with other techniques.

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Section: Discussionmentioning

confidence: 99%

“…Hossain [22] Proposed model Rady and Anwar [14] Rubini and Perumal [16] Elhoseny et al [18] Chittora et al [20] M.M. Hossain [22] Proposed model…”

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Enhanced Optimized Classification Model of Chronic Kidney Disease

Elkholy¹,

Rezk²,

Saleh³

2023

IJACSA

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“…Customer feedback is critical for every business, including restaurants. Companies may use customer reviews to learn more about their products (Hossain et al. , 2023a).…”

Section: Introductionmentioning

confidence: 99%

“…Organizations can aggregate all customer evaluations from several platforms in one centralized spot, allowing them to reply to the feedback (Adak et al. , 2022; Hossain et al. , 2023b).…”

Section: Introductionmentioning

confidence: 99%

Investigation and prediction of users' sentiment toward food delivery apps applying machine learning approaches

Hossain

Begum

Rouf

et al. 2023

JCMARS

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PurposeThe goal of the current research is to use different machine learning (ML) approaches to examine and predict customer reviews of food delivery apps (FDAs).Design/methodology/approachUsing Google Play Scraper, data from five food delivery service providers were collected from the Google Play store. Following cleaning the reviews, the filtered texts were classified as having negative, positive, or neutral sentiments, which were then scored using two unsupervised sentiment algorithms (AFINN and Valence Aware Dictionary for sentiment Reasoning (VADER)). Furthermore, the authors employed four ML approaches to categorize each review of FDAs into the respective sentiment class.FindingsAccording to the study's findings, the majority of customer reviews of FDAs were positive. This research also revealed that, while all of the methods (decision tree, linear support vector machine, random forest classifier and logistic regression) can appropriately classify the reviews into a sentiment category, support vector machines (SVM) beats the others in terms of model accuracy. The authors' study also showed that logistic regression provided the highest recall, F1 score and lowest Root Mean Square Error (RMSE) among the four ML models.Practical implicationsThe findings aid FDAs in determining customer review behavior. The study's findings could help food apps developers better understand how customers feel about the developers' products and services. The food apps developer can learn how to use ML techniques to better understand the users' behavior.Originality/valueThe current study uses ML methodologies to investigate and predict consumer attitude regarding FDAs.

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Improved vehicle detection: unveiling the potential of modified YOLOv5

Rana,

Hossain,

Hossain

et al. 2024

Discov Appl Sci

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The detection of vehicles is a crucial task in various applications. In recent years, the quantity of vehicles on the road has been rapidly increasing, resulting in the challenge of efficient traffic management. To address this, the study introduces a model of enhancing the accuracy of vehicle detection using a proposed improved version of the popular You Only Look Once (YOLO) model, known as YOLOv5. The accuracy of vehicle detection using both the original versions of YOLOv5 and our proposed YOLOv5 algorithm has been evaluated. The evaluation is based on key accuracy metrics such as precision, recall, and mean Average Precision (mAP) at an Intersection over Union (IoU). The study's experimental results show that the original YOLOv5 model achieved a mean Average Precision (mAP) of 61.4% and the proposed model achieved an mAP of 67.4%, outperforming the original by 6%. The performance of the proposed model was improved based on the architectural modifications, which involved adding an extra layer to the backbone. The results reveal the potential of our proposed YOLOv5 for real-world applications such as autonomous driving and traffic monitoring and may involve further fine-tuning, robotics and security system and exploring broader object detection domains.

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Analysis of the performance of feature optimization techniques for the diagnosis of machine learning-based chronic kidney disease

Cited by 9 publications

References 10 publications

Enhanced Optimized Classification Model of Chronic Kidney Disease

Enhanced Optimized Classification Model of Chronic Kidney Disease

Investigation and prediction of users' sentiment toward food delivery apps applying machine learning approaches

Improved vehicle detection: unveiling the potential of modified YOLOv5

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