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- 1. 1 Paper Id: ICACRS-320 PaperTitle: Review Of Literature On Severe Kidney Chronic Disease Syndrome Using Various Biological Parameter under Machine Learning Techniques Author Name(s): M.Suresh, E.Nirmala, M. Maragatharajan, Manish Yadav Name of Presenter: Manish Yadav Date of Presentation: 06th December 2024 Presentation on Literature On Severe Kidney Chronic Disease Syndrome Using Various Biological Parameter under Machine Learning Techniques ICACRS 2024 conference
- 2. 2 Agenda ICACRS 2024 conference •Introduction •ProblemStatement and Motivation •Objectives •Literature Review •Methodology •Results and Discussion •Future Work •Conclusion
- 3. 3 Introduction ICACRS 2024 conference ·What is CKD? Chronic Kidney Disease (CKD) is a long-term condition characterized by the kidney's inability to filter blood properly. It can lead to severe health issues, including organ failure. · Current Challenges: Many cases of CKD remain undiagnosed until advanced stages, making treatment less effective. Key contributing factors include diabetes, hypertension, and environmental conditions. · Relevance of Machine Learning (ML): Advances in ML and data analytics have provided powerful tools to analyze large datasets, enabling early detection and diagnosis of diseases like CKD. · Objective of the Research: The study aims to analyze existing literature on ML applications in predicting CKD, focusing on biological parameters, pre-processing techniques, and model validation methods.
- 4. 4 Problem Statement andMotivation ICACRS 2024 conference · Problem Statement: Traditional diagnostic methods for CKD often fail to detect the disease in its early stages. High variability in datasets and a lack of feature selection methods limit the accuracy and generalizability of existing models. Overfitting and validation challenges undermine the reliability of many ML-based predictions. · Motivation: Growing prevalence of CKD necessitates efficient diagnostic tools. Improved prediction can reduce healthcare costs, enhance treatment outcomes, and save lives. Leveraging ML techniques offers the potential to achieve high precision and accuracy while addressing gaps in data handling and validation.
- 5. 5 Objectives ICACRS 2024 conference Primary Goals: o Evaluate the effectiveness of ML models in CKD prediction. o Analyze pre-processing techniques like feature selection and imputation. o Examine validation methods to ensure accuracy and reliability of predictions. Specific Deliverables: o Identification of the best-performing models and techniques based on accuracy and precision. o Recommendations for improving ML workflows in CKD research. o Insights into challenges like overfitting and dataset quality.
- 6. 6 Literature Review ICACRS 2024conference •Key Findings from Reviewed Articles: • Techniques Explored: • Supervised Rule Mining (e.g., L2-regularized Logistic Regression) excels in local predictive modeling. • Hybrid models like SVM-CNN demonstrate superior accuracy by combining classification and feature extraction techniques. • Model Performance: • Random Forest, Decision Trees, and boosting algorithms like AdaBoost achieve accuracies above 99%. • Neural Networks show promise but require careful validation to prevent overfitting. • Validation Challenges: • Many studies report inflated accuracies due to poor handling of small datasets or missing values. • Biological Parameter Usage: • Effective models emphasize the importance of reducing dataset dimensionality without losing critical information.
- 7. 7 Literature Review ICACRS 2024conference •Gaps Identified: • Limited use of advanced feature selection methods like TabNet or AutoML. • Insufficient diversity in datasets to generalize findings across populations.
- 8. 8 Methodology ICACRS 2024 conference •DataPre-processing: • Techniques include quantile-based discretization, univariate feature selection (UFS), and recursive feature elimination (RFE). • Missing data is addressed using imputation techniques like KNN and matrix factorization. •ML Techniques Reviewed: • Classical models: SVM, Decision Trees, Random Forest, Naïve Bayes. • Advanced models: Hybrid SVM-CNN and boosting algorithms. •Validation Methods: • Cross-validation (e.g., K-fold, 10-fold). • Performance metrics like MCC, F1-score, and ROC-AUC. •Feature Selection: • Prioritizing attributes like demographic data and biomarkers to improve accuracy.
- 9. 9 Results and Discussion ICACRS2024 conference · Comparison of Models: Top Performers: Random Forest and LogitBoost with accuracies >99%. Hybrid Models: SVM-CNN outperformed traditional CNN by achieving an accuracy of 96.59%. Ensemble Methods: Improved generalizability through bagging and boosting. · Key Insights: Feature selection enhances model precision while reducing computational costs. Overfitting remains a critical issue, with some studies reporting impossible 100% accuracy due to small or imbalanced datasets.
- 10. 10 Results and Discussion ICACRS2024 conference Strengths and Weaknesses of Reviewed Models: Strength: High accuracy and ease of interpretation in models like Random Forest. Weakness: Computational inefficiencies and limited scalability in neural network models.
- 11. 11 Future Work ICACRS 2024conference · Addressing Data Limitations: Focus on larger, more diverse datasets to improve generalizability. Use real-time data collection to enhance model relevance. · Advanced Techniques: Apply novel methods like Generative Adversarial Networks (GANs) for imputing missing data. Incorporate stacked ensemble techniques to improve predictive power. · Validation Enhancements: Develop robust protocols to detect and mitigate overfitting. Incorporate explainable AI to enhance clinical interpretability. · Exploring Non-invasive Diagnostics: Investigate saliva-based diagnostic models to reduce patient discomfort.
- 12. 12 Conclusion ICACRS 2024 conference ·Summary of Findings: ML techniques provide powerful tools for early detection of CKD, achieving accuracies above 99% in many cases. Pre-processing and feature selection significantly impact model performance. Challenges like overfitting and dataset limitations need to be addressed. · Call to Action: Encourage further research on hybrid and ensemble models. Emphasize clinical collaborations to refine and implement ML models in real-world scenarios. · Final Note: Reliable and interpretable ML models can revolutionize CKD diagnostics, improving patient outcomes and reducing healthcare costs.
- 13. 13 Thank You HAVE AGREAT DAY! ICACRS 2024 conference