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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: May 2026
Volume 11 | Issue 5
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Impact Factor : 8.14
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| Paper Title: | Enhanced heart failure survival prediction using Machine Learning and SMOTENC |
| Authors Name: | Shaik Mahamad Shakeer , Papisetty Lahari , Batta Pavani , Kattubadi Munawar Hussain |
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IJRTI_211115
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| Published Paper Id: | IJRTI2604245 |
| Published In: | Volume 11 Issue 4, April-2026 |
| DOI: | |
| Abstract: | Worsening ventricular contractile dysfunction in heart failure (HF) patients triggers a deterioration cycle that raises short-term fatality risk while imposing heavy burdens on acute care infrastructure. Building reliable mortality prog- nosis models from retrospective clinical records confronts two interconnected structural obstacles: severe outcome skew pro- ducing minority-class under-representation, and the coexistence of continuous physiological measurements with binary medi- cal flags that invalidate continuous-only interpolation during data augmentation. This paper presents a four-stage learning framework targeting both obstacles. An ANOVA F-statistic filter reduces a twelve-variable clinical record to the six most outcome- discriminative attributes. The Synthetic Minority Over-sampling Technique for Nominal and Continuous features (SMOTENC) then generates class-balanced training samples by routing con- tinuous dimensions through linear interpolation and categorical dimensions through neighbor-majority voting, thereby preserving valid discrete states in every synthetic record. Adaptive Inertia Weight Particle Swarm Optimization (AIW-PSO) — directed by an Akaike Information Criterion (AIC) fitness that jointly penalizes prediction error and structural complexity — locates the Gradient Boosting Machine (GBM) configuration that best balances accuracy and parsimony. The tuned GBM is trained on the augmented partition and assessed on a sealed held-out test set. On the UCI Heart Failure Clinical Records benchmark, the framework achieves 95% classification accuracy, AUC of 0.96, and mortality-class F1-score of 0.89 — outperforming logistic regression, support vector machine, random forest, and untuned GBM baselines in every reported dimension. Ablation experiments confirm that SMOTENC contributes a 4-point recall advantage over standard SMOTE, while AIC-guided tuning adds a further 5-point accuracy gain over default GBM configuration. |
| Keywords: | heart failure prognosis, SMOTENC, gradient boosting, AIW-PSO, AIC optimization, class imbalance, clinical machine learning, ANOVA feature selection |
| Cite Article: | "Enhanced heart failure survival prediction using Machine Learning and SMOTENC", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.b776-b783, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604245.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2604245
Registration ID:211115
Published In: Volume 11 Issue 4, April-2026
DOI (Digital Object Identifier):
Page No: b776-b783 Country: Nandyal, Andhrapradesh, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2604245 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2604245 |
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ISSN: 2456-3315
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