An Ensembled Framework for Human Breast Cancer Survivability Prediction Using Deep Learning

Nested genetic algorithm-based classifier selection and placement in multi-level ensemble framework for effective disease diagnosis

Effective disease diagnosis is a critical unmet need on a global scale. The intricacies of the numerous disease mechanisms and underlying symptoms make developing a model for early diagnosis and effective treatment extremely difficult. Machine learning (ML) can help to solve some of these issues. Recently, various ensemble-based ML models have benefited clinicians in early diagnosis. However, one of the most difficult challenges in multi-level ensemble approaches is the classifier selection and their placement in the ensemble framework as it improves the overall performance. Let m classifiers have to select from n classifiers there are ( n m ) ways. Again, these ( n m ) possibilities can be arranged in m ! ways. Finding the best m classifiers and their positions from total ( n m ) m ! ways is a challenging and hard problem. To address this challenge, a dynamic three-level ensemble framework is proposed. A nested Genetic Algorithm (GA) and ensemble-based fitness function are employed to optimize the classifier selection and their placement in a three-level ensemble framework. Our approach used eleven classifiers and chose seven classifiers by maximizing the fitness function. The proposed model experiments on 12 disease datasets. The proposed model outperformed in terms of accuracy, F1, and G-measure on the Chronic Kidney Disease (CKD) dataset is 0.987, 0.988, and 0.989, respectively. In terms of AUC on the Heart disease dataset (HDD) is 0.998 and in terms of recall on the Hypothyroid disease dataset (HyDD) is 0.988. In addition, the proposed model superiority is statically evaluated by Wilcoxon-Signed-Rank (WSR) test compared with other ensemble models, such as random forest (RF), bagging classifier (BC), XGBoost (XGB), and gradient boost classifier (GBC) with probability value p < 0.05 results shows all the traditional ensemble model differs with proposed model and also effective size evaluated with using the matched-pairs rank biserial correlation coefficient wc and statistical results shows effective size is large with RF and BC and effective size is medium with XGB and GBC. Proposed model has outperformed comparing with State-Of-The-Art (SOTA) ensemble and non-ensemble models. Further, the proposed model outperformed in terms of the ROC curve in the majority of the disease datasets. The results suggest the usage of the proposed model for disease diagnosis applications.

TDMFS: Tucker decomposition multimodal fusion model for pan-cancer survival prediction

Integrated analysis of multimodal data offers a more comprehensive view for cancer survival prediction, yet it faces challenges like computational intensity, overfitting, and challenges in achieving a unified representation due to data heterogeneity. To address the above issues, the first Tucker decomposition multimodal fusion model was hereby proposed for pan-cancer survival prediction (TDMFS). The model employed Tucker decomposition to limit complex tensor parameters during fusion, achieving deep modality integration with reduced computational cost and lower overfitting risk. The individual modality-specific representations were then fully exploited by signal modulation mechanisms in a bilinear pooling decomposition to serve as complementary information for the deep fusion representation. Furthermore, the performance of TDMFS was evaluated using a 5-fold cross-validation method with two modal data, gene expression (GeneExpr), and copy number variation (CNV), for 33 cancers from The Cancer Genome Atlas (TCGA) database. The experiments demonstrated that the proposed TDMFS model achieved an average C-index of 0.757 across 33 cancer datasets, with a C-index exceeding 0.80 on 10 of these datasets. Survival curves for both high and low risk patients plotted on 27 cancer datasets were statistically significant. The TDMFS model demonstrated superior performance in survival prediction, outperforming models like LinearSum and Multimodal Factorisation Higher Order Pooling, making it a valuable asset for advancing clinical cancer research.

Artificial intelligence in breast cancer survival prediction: a comprehensive systematic review and meta-analysis

Background

Breast cancer (BC), as a leading cause of cancer mortality in women, demands robust prediction models for early diagnosis and personalized treatment. Artificial Intelligence (AI) and Machine Learning (ML) algorithms offer promising solutions for automated survival prediction, driving this study's systematic review and meta-analysis.

Methods

Three online databases (Web of Science, PubMed, and Scopus) were comprehensively searched (January 2016-August 2023) using key terms ("Breast Cancer", "Survival Prediction", and "Machine Learning") and their synonyms. Original articles applying ML algorithms for BC survival prediction using clinical data were included. The quality of studies was assessed via the Qiao Quality Assessment tool.

Results

Amongst 140 identified articles, 32 met the eligibility criteria. Analyzed ML methods achieved a mean validation accuracy of 89.73%. Hybrid models, combining traditional and modern ML techniques, were mostly considered to predict survival rates (40.62%). Supervised learning was the dominant ML paradigm (75%). Common ML methodologies included pre-processing, feature extraction, dimensionality reduction, and classification. Deep Learning (DL), particularly Convolutional Neural Networks (CNNs), emerged as the preferred modern algorithm within these methodologies. Notably, 81.25% of studies relied on internal validation, primarily using K-fold cross-validation and train/test split strategies.

Conclusion

The findings underscore the significant potential of AI-based algorithms in enhancing the accuracy of BC survival predictions. However, to ensure the robustness and generalizability of these predictive models, future research should emphasize the importance of rigorous external validation. Such endeavors will not only validate the efficacy of these models across diverse populations but also pave the way for their integration into clinical practice, ultimately contributing to personalized patient care and improved survival outcomes.

Systematic Review Registration

https://www.crd.york.ac.uk/prospero/, identifier CRD42024513350.