Deep Learning-Based Multi-Modal Ensemble Classification Approach for Human Breast Cancer Prognosis

Jadoon, Ehtisham Khan; Khan, Fiaz Gul; Shah, Sayyar Ali; Khan, Ahmad; ElAffendi, Mohammed

doi:10.1109/access.2023.3304242

Cited by 10 publications

(1 citation statement)

References 41 publications

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“…Despite the fact that there have been a lot of prior studies on model performances, comparisons, and improving classification outcomes ( Jadoon et al, 2023 ; Mujahid et al, 2023 ). Such works are often limited to technical improvements instead of explaining the black-box models for clinician decision-making purposes.…”

Section: Introductionmentioning

confidence: 99%

Predicting inmate suicidal behavior with an interpretable ensemble machine learning approach in smart prisons

Akhtar,

Yaseen,

Imran

et al. 2024

PeerJ Computer Science

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The convergence of smart technologies and predictive modelling in prisons presents an exciting opportunity to revolutionize the monitoring of inmate behaviour, allowing for the early detection of signs of distress and the effective mitigation of suicide risks. While machine learning algorithms have been extensively employed in predicting suicidal behaviour, a critical aspect that has often been overlooked is the interoperability of these models. Most of the work done on model interpretations for suicide predictions often limits itself to feature reduction and highlighting important contributing features only. To address this research gap, we used Anchor explanations for creating human-readable statements based on simple rules, which, to our knowledge, have never been used before for suicide prediction models. We also overcome the limitation of anchor explanations, which create weak rules on high-dimensionality datasets, by first reducing data features with the help of SHapley Additive exPlanations (SHAP). We further reduce data features through anchor interpretations for the final ensemble model of XGBoost and random forest. Our results indicate significant improvement when compared with state-of-the-art models, having an accuracy and precision of 98.6% and 98.9%, respectively. The F1-score for the best suicide ideation model appeared to be 96.7%.

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Section: Introductionmentioning

confidence: 99%