ML-CKDP: Machine learning-based chronic kidney disease prediction with smart web application

Journal of Pathology Informatics, Journal Year: 2024, Volume and Issue: 15, P. 100371 - 100371

Published: Feb. 22, 2024

Chronic kidney diseases (CKDs) are a significant public health issue with potential for severe complications such as hypertension, anemia, and renal failure. Timely diagnosis is crucial effective management. Leveraging machine learning within healthcare offers promising advancements in predictive diagnostics. In this paper, we developed learning-based prediction (ML‐CKDP) model dual objectives: to enhance dataset preprocessing CKD classification develop web-based application prediction. The proposed involves comprehensive data protocol, converting categorical variables numerical values, imputing missing data, normalizing via Min-Max scaling. Feature selection executed using variety of techniques including Correlation, Chi-Square, Variance Threshold, Recursive Elimination, Sequential Forward Selection, Lasso Regression, Ridge Regression refine the datasets. employs seven classifiers: Random Forest (RF), AdaBoost (AdaB), Gradient Boosting (GB), XgBoost (XgB), Naive Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT), predict CKDs. effectiveness models assessed by measuring their accuracy, analyzing confusion matrix statistics, calculating Area Under Curve (AUC) specifically positive cases. (RF) (AdaB) achieve 100% accuracy rate, evident across various validation methods splits 70:30, 80:20, K-Fold set 10 15. RF AdaB consistently reach perfect AUC scores multiple datasets, under different splitting ratios. Moreover, (NB) stands out its efficiency, recording lowest training testing times all datasets split Additionally, present real-time operationalize model, enhancing accessibility practitioners stakeholders. Web app link: https://rajib-research-kedney-diseases-prediction.onrender.com/

Language: Английский

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Examining inclusivity: the use of AI and diverse populations in health and social care: a systematic review

BMC Medical Informatics and Decision Making, Journal Year: 2025, Volume and Issue: 25(1)

Published: Feb. 5, 2025

Abstract Background Artificial intelligence (AI)-based systems are being rapidly integrated into the fields of health and social care. Although such can substantially improve provision care, diverse marginalized populations often incorrectly or insufficiently represented within these systems. This review aims to assess influence AI on care among populations, particularly with regard issues related inclusivity regulatory concerns. Methods We followed Preferred Reporting Items for Systematic Reviews Meta-Analyses guidelines. Six leading databases were searched, 129 articles selected this in line predefined eligibility criteria. Results research revealed disparities outcomes, accessibility, representation groups due biased data sources a lack training datasets, which potentially exacerbate inequalities delivery communities. Conclusion development practices, legal frameworks, policies must be reformulated ensure that is applied an equitable manner. A holistic approach used address disparities, enforce effective regulations, safeguard privacy, promote inclusion equity, emphasize rigorous validation.

Language: Английский

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A hybrid mental health prediction model using Support Vector Machine, Multilayer Perceptron, and Random Forest algorithms

Healthcare Analytics, Journal Year: 2023, Volume and Issue: 3, P. 100185 - 100185

Published: May 2, 2023

The prevalence and burden of mental health disorders are on the rise in conflict zones, if left untreated, they can lead to considerable lifetime disability. Following repeal Article 370, political unrest spread quickly, forcing Indian government impose safety precautions such as lockdowns communication ban. Consequently, region Kashmir experienced a marked anxiety result these lifestyle changes. Machine learning has proven useful early diagnosis prognosis certain diseases. Therefore, this study aims classify problems by utilising pre-clinical dataset collected after abrogation article 370 Kashmir. first part paper at developing implementing prediction model based classification into one five stages, i.e., Stage 1: minimal anxiety, 2: mild 3: moderate 4: severe 5: very anxiety. second offers recommendations for those suffering from disorders. Feature selection used predict correct stage best possible medical intervention. Three different algorithms: Support Vector Machine(SVM), Multilayer Perceptron (MLP), Random Forest (RF), employed predicting stages. Among them, random forest (RF) achieved 98.13% accuracy. A forecasted likelihood condition was assessed provide suitable recommendation. Further, accuracy kappa statistics assess performance suggested model, which significant addition early, exhibits high recommendation This assist professionals experts making quick accurate choices.

Language: Английский

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Data analytics in public health, A USA perspective: A review

World Journal of Advanced Research and Reviews, Journal Year: 2023, Volume and Issue: 20(3), P. 211 - 224

Published: Dec. 4, 2023

The integration of data analytics into public health practices represents a transformative paradigm shift in the United States. This review provides comprehensive analysis impact and implications on strategies, with focus disease surveillance policy within USA. In context surveillance, has emerged as crucial tool for real-time monitoring early detection threats. Leveraging diverse datasets, including electronic records social media, allows swift identification trends anomalies, enabling proactive responses to potential outbreaks. Advanced techniques, such machine learning predictive modeling, contribute precision efforts, facilitating targeted interventions resource allocation. Beyond significantly influences policy. Evidence-based formulation is enhanced through data-driven insights, providing policymakers foundation understanding designing strategies that align unique needs populations. Resource allocation are optimized, ensuring efficient use limited resources by analyzing outcomes, service utilization patterns, cost-effectiveness. Continuous evaluation implemented policies enable adapt response evolving challenges, fostering dynamic adaptive ecosystem. As landscape evolves, USA continues play central role shaping policies. study delves historical context, key components, applications, success stories, valuable insights policymakers, professionals, researchers aiming navigate complexities management.

Language: Английский

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Toward Comprehensive Chronic Kidney Disease Prediction Based on Ensemble Deep Learning Models

Applied Sciences, Journal Year: 2023, Volume and Issue: 13(6), P. 3937 - 3937

Published: March 20, 2023

Chronic kidney disease (CKD) refers to the gradual decline of function over months or years. Early detection CKD is crucial and significantly affects a patient’s decreasing health progression through several methods, including pharmacological intervention in mild cases hemodialysis transportation severe cases. In recent past, machine learning (ML) deep (DL) models have become important medical diagnosis domain due their high prediction accuracy. The performance developed model mainly depends on choosing appropriate features suitable algorithms. Accordingly, paper aims introduce novel ensemble DL approach detect CKD; multiple methods feature selection were used select optimal selected features. Moreover, we study effect chosen from side. proposed integrates pretrained with support vector (SVM) as metalearner model. Extensive experiments conducted by using 400 patients UCI repository. results demonstrate efficiency compared other models. mutual_info_classi obtained highest performance.

Language: Английский

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Integrating Artificial Intelligence in Human Resource Management: A SmartPLS Approach for Entrepreneurial Success

Aptisi Transactions On Technopreneurship (ATT), Journal Year: 2023, Volume and Issue: 5(3), P. 334 - 345

Published: Nov. 30, 2023

The primary focus of this research is to examine the pivotal role Artificial Intelligence (AI) in driving business transformation, with a specific emphasis on its impact within realm human resource management (HR). study seeks assess substantial influence brought about by incorporation AI HR. Online data collection involved 110 respondents professional backgrounds In pursuit enhancing entrepreneurial success, adopts Smart Partial Least Square (Smart PLS) approach seamlessly integrate artificial intelligence into HR management. analysis using PLS delves examination AI's effects recruitment process, employee development, and performance findings reveal that utilization significantly expedites processes, enhances decision accuracy, positively contributes attainment objectives. practical implications these outcomes are thoroughly discussed, potential avenues for future outlined. This not only provides valuable insights stakeholders but also offers guidance optimizing application context

Language: Английский

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A new intelligent approach of surface roughness measurement in sustainable machining of AM-316L stainless steel with deep learning models

Measurement, Journal Year: 2024, Volume and Issue: 230, P. 114515 - 114515

Published: March 15, 2024

Language: Английский

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A cluster-based human resources analytics for predicting employee turnover using optimized Artificial Neural Networks and data augmentation

Decision Analytics Journal, Journal Year: 2024, Volume and Issue: 11, P. 100461 - 100461

Published: April 15, 2024

This study presents an innovative methodology to predict employee turnover by integrating Artificial Neural Networks (ANN) with clustering techniques. We focus on hyperparameter tuning various input parameters obtain optimal ANN models. By segmenting data, the identifies critical predictors, allowing targeted interventions be implemented improve efficiency and effectiveness of retention policies. Data augmentation using Conditional Generative Adversarial (CTGAN) is performed clusters imbalanced data. Following this, optimized models are applied these augmented clusters, leading a notable improvement in their performance. evaluate our against five other variants four traditional machine learning demonstrate superior accuracy recall. The proposed approach achieves operational advantages shifting away from generalized strategies more focused, cluster-based policies, which can optimize resource utilization reduce costs. Because its practicality enhanced ability manage turnover, this method, supported empirical evidence, significant advancement human (HR) analytics

Language: Английский

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A machine learning approach for detecting customs fraud through unstructured data analysis in social media

Decision Analytics Journal, Journal Year: 2024, Volume and Issue: 10, P. 100408 - 100408

Published: Feb. 2, 2024

Goods and services are sold through social media by individuals not authorized as legitimate dealers, resulting in lost taxes customs duties to governments. This study proposes a model called SHIELD for detecting these violations unstructured data media. The process involves collecting 2,373,570 records of commercial goods from platforms such Twitter Facebook three phases. In Phase 1, keywords labeling collected text classification. Three categories results defined: Red Line smuggled goods, unpaid duty, prohibited restricted goods; Green non-commercial Inspect that cannot be identified the require further investigation. 2 3 use detect smugglers grouped algorithms Logistic Regression (LR), Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), employed classify imported illegal products. all tests show LSTM technique had best accuracy 99.44% average F1 score 90.55%. Using techniques LR, GRU, demonstrates potential machine learning natural language processing activities promoting economic security.

Language: Английский

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Artificial Intelligence in Kidney Disease: A Comprehensive Study and Directions for Future Research

Diagnostics, Journal Year: 2024, Volume and Issue: 14(4), P. 397 - 397

Published: Feb. 12, 2024

Artificial intelligence (AI) has emerged as a promising tool in the field of healthcare, with an increasing number research articles evaluating its applications domain kidney disease. To comprehend evolving landscape AI disease, bibliometric analysis is essential. The purposes this study are to systematically analyze and quantify scientific output, trends, collaborative networks application This collected AI-related published between 2012 20 November 2023 from Web Science. Descriptive analyses trends disease were used determine growth rate publications by authors, journals, institutions, countries. Visualization network maps country collaborations author-provided keyword co-occurrences generated show hotspots on initial search yielded 673 articles, which 631 included analyses. Our findings reveal noteworthy exponential trend annual

Language: Английский

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