Biomedical visual data analysis to build an intelligent diagnostic decision support system in medical genetics
作者：
Highlights：
• The proposed methodology, visual diagnostic DSS, employs ML algorithms and image processing techniques for automated diagnosis in medical genetics.
• The proposed system was trained using a real dataset of previously published face images of subjects with syndromes.
• A high accuracy rate was achieved using this automated diagnosis technique.
• The results show that the accurate classification of syndromes is feasible using ML techniques.
• The study demonstrates the benefits of using hybrid image processing and ML-based computer-aided diagnostics for identifying facial phenotypes.
摘要
Highlights•The proposed methodology, visual diagnostic DSS, employs ML algorithms and image processing techniques for automated diagnosis in medical genetics.•The proposed system was trained using a real dataset of previously published face images of subjects with syndromes.•A high accuracy rate was achieved using this automated diagnosis technique.•The results show that the accurate classification of syndromes is feasible using ML techniques.•The study demonstrates the benefits of using hybrid image processing and ML-based computer-aided diagnostics for identifying facial phenotypes.
论文关键词：Decision support system,Machine learning,Visual data analysis,Principal components analysis,Medical genetics,Dysmorphology,Facial genotype–phenotype
论文评审过程：Received 12 May 2013, Revised 15 August 2014, Accepted 16 August 2014, Available online 23 August 2014.
论文官网地址：https://doi.org/10.1016/j.artmed.2014.08.003
