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The Establishment of Hypertrophic Cardiomyopathy Diagnosis Model via Artificial Neural Network and Random Decision
Shuanglei Li1, Zekun Feng1, Cangsong Xiao1
1Division of Adult Cardiac Surgery, Department of Cardiology, The Sixth Medical Center, Chinese PLA General Hospital, Beijing 100037, China.
Insights
This study developed a novel diagnostic model for hypertrophic cardiomyopathy (HCM) using machine learning. The model aids in early detection, improving patient survival and quality of life.
Area of Science:
- Cardiovascular Genetics
- Molecular Biology
- Bioinformatics
Background:
- Hypertrophic cardiomyopathy (HCM) is an inherited cardiac condition marked by ventricular hypertrophy.
- Current diagnostic methods lack effectiveness for early detection of HCM.
- There is a critical need for advanced diagnostic tools for timely intervention.
Purpose of the Study:
- To develop and validate a predictive diagnostic model for hypertrophic cardiomyopathy.
- To identify key genetic markers for early HCM detection.
- To improve patient outcomes through early diagnosis and treatment.
Main Methods:
- Integrated multiple Gene Expression Omnibus (GEO) datasets for RNA profiling.
- Utilized random decision forests to identify characteristic genes.
- Trained an artificial neural network to construct a predictive scoring model (MPS).
Main Results:
- Identified 642 differentially expressed genes and narrowed down to 46 characteristic genes.
- Developed an MPS model with high prediction performance, validated by ROC curve analysis.
- Demonstrated the model's efficacy in predicting hypertrophic cardiomyopathy.
Conclusions:
- A novel diagnostic model combining random forest and artificial neural networks was successfully developed.
- The MPS model shows promise for the early prediction of hypertrophic cardiomyopathy.
- This approach aims to enhance early treatment, prolong survival, and improve patient quality of life.
Abstract:
Hypertrophic cardiomyopathy is a hereditary disease characterized by asymmetric ventricular hypertrophy as the key anatomical feature. Currently, there exists no effective method for the early diagnosis of hypertrophic cardiomyopathy. In this analysis, we incorporated multiple GEO datasets containing RNA profiles of hypertrophic cardiomyopathic patient tissues, identified 642 differentially expressed genes, and performed GO and KEGG analyses. Furthermore, we narrowed down 46 characteristic genes from these differentially expressed genes using random decision forests and conducted transcription factor regulation analysis on them. Using 40 genes that showed overlap between the training set and the verification set, the artificial neural network was trained, and the final MPS scoring model was constructed, and a receiver-operating characteristic (ROC) curve was drawn. We used the MPS model to predict the verification dataset and drew the ROC curve, which demonstrated the good prediction performance of the model. In conclusion, this study combines a random decision forest and artificial neural network to build a diagnostic model for hypertrophic cardiomyopathy to predict the disease, aiming at early detection and treatment, prolonging the survival time, and improving the quality of life of patients.
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