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Human-Guided Feature Selection for Accurate Cardiomyocyte Dysfunction Classification
Insights
Identifying early cardiomyocyte dysfunction in diastolic heart failure is crucial. This study developed a feature selection method using random forest classification to pinpoint key cellular markers for accurate diagnosis.
Area of Science:
- Cardiovascular Biology
- Computational Biology
- Biomedical Engineering
Background:
- Diastolic heart failure (DHF) diagnosis hinges on early identification of cardiomyocyte dysfunction, specifically impaired left ventricular relaxation (ILVR).
- Intracellular calcium handling is vital for myocardial relaxation; impaired calcium removal during diastole leads to ILVR.
- Analyzing sarcomere length (SL) and intracellular calcium kinetics (CK) is essential for cellular-level relaxation characterization, but data complexity poses challenges.
Purpose of the Study:
- To develop a robust feature selection pipeline for identifying informative features from SL and CK data.
- To create an effective classifier for early detection of cardiomyocyte dysfunction using reduced feature sets.
- To compare the performance of a selected feature set against full datasets and PCA-derived features.
Main Methods:
- Utilized statistical significance testing (p-values), hierarchical clustering, and random forest (RF) classification for feature selection.
- Obtained SL and CK transients from a transgenic mouse model with ILVR (AAA mice) and wild-type controls (NTG).
- Trained and evaluated RF classifiers using reduced feature sets, full feature sets, and principal component analysis (PCA)-based dimension reduction.
Main Results:
- A reduced feature set, selected through the pipeline, achieved classification performance comparable to the full feature set.
- The selected feature set outperformed PCA-based dimension reduction in classifying cardiomyocyte dysfunction.
- The reduced feature set enhanced interpretability by retaining biologically relevant features.
Conclusions:
- A carefully selected, small set of biological features can effectively detect early signs of cardiomyocyte dysfunction.
- The developed feature selection pipeline offers a robust method for analyzing complex SL and CK data.
- This approach aids in the early prognosis of diastolic heart failure by identifying cellular-level abnormalities.
Abstract:
Early identification of cardiomyocyte dysfunction is a critical challenge for the prognosis of diastolic heart failure (DHF) exhibiting impaired left ventricular relaxation (ILVR). Myocardial relaxation relies strongly on efficient intracellular calcium (${\text{Ca}}^{2+}$) handling. During diastole, a sluggish removal of ${\text{Ca}}^{2+}$ from cardiomyocytes disrupts sarcomere relaxation, leading to ILVR \textit{at the organ level}. Characterizing myocardial relaxation \textit{at the cellular level} requires analyzing both sarcomere length (SL) transients and intracellular calcium kinetics (CK). However, due to the complexity and redundancy in SL and CK data, identifying the most informative features for accurate classification is challenging. To address this, we developed a robust feature selection pipeline involving statistical significance testing (p-values), hierarchical clustering, and feature importance evaluation using random forest (RF) classification to select the most informative features from SL and CK data. SL and CK transients were obtained from prior studies involving a transgenic phospho-ablated mouse model exhibiting ILVR (AAA mice) and wild-type as non-transgenic control mice (NTG). By iteratively refining the feature set, we trained a RF classifier using the selected reduced features. For comparison, we evaluated the performance of the classifier using the full set of original features as well as a dimensionally reduced set derived through principal component analysis (PCA). The confusion matrices demonstrated that the reduced feature set achieved comparable performance to the full feature set and outperformed the PCA-based approach, while offering better interpretability by retaining biologically relevant features. These findings suggest that a small, carefully chosen set of biological features can effectively detect early signs of cardiomyocyte dysfunction.
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