A Quantitative Comparison of Different Machine Learning Approaches for Human Spermatozoa Quality Prediction Using Multimodal Datasets

Despite remarkable advances in medical data analysis fields, they are severely restrained from the limited property of the employed single modality, usually medical imaging data. However, other modalities (such as patient-related information) should also be taken into account in the process of clinical decision. How to fully employ the multi-modal dataset is still under-explored. In this paper, we make a quantitative comparison of different machine learning approaches for the human spermatozoa quality prediction task, leveraging multiple modalities dataset. To empirically investigate the advantages and disadvantages of different machine learning approaches, we perform extensive experiments. Leveraging different features, we achieve state-of-the-art performance on most of the tasks. The obtained results show that simple models can provide better performance, which emphasizes the importance of avoiding overfitting. For the sake of reproducibility, we have released our code to facilitate the research community(1).