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This research aims to explore which kinds of metrics are more valuable in making investment decisions for a venture capital firm using machine learning methods. We measure the fit of developed companies to a venture capital firm’s investment thesis with a balanced scorecard based on quantitative and qualitative characteristics of the companies. Collaborating with the management team of Rose Street Capital (RSC), we explore the most influential factors of their balanced scorecard using their retrospective investment decisions of successful and failed startup companies. Our study employs six standard machine learning models and their counterparts with an additional feature selection technique. Our findings suggest that “planning strategy” and “team management” are the two most determinant factors in the firm’s investment decisions, implying that qualitative factors could be more important to startup evaluation. Furthermore, we analyzed which machine learning models were most accurate in predicting the firm’s investment decisions. Our experimental results demonstrate that the best machine learning models achieve an overall accuracy of 78% in making the correct investment decisions, with an average of 87% and 69% in predicting the decision of companies the firm would and would not have invested in, respectively. Our study provides convincing evidence that qualitative criteria could be more influential in investment decisions and machine learning models can be adapted to help provide which values may be more important to consider for a venture capital firm.
Sarah Bai; Yijun Zhao. Startup Investment Decision Support: Application of Venture Capital Scorecards Using Machine Learning Approaches. Systems 2021, 9, 55 .
AMA StyleSarah Bai, Yijun Zhao. Startup Investment Decision Support: Application of Venture Capital Scorecards Using Machine Learning Approaches. Systems. 2021; 9 (3):55.
Chicago/Turabian StyleSarah Bai; Yijun Zhao. 2021. "Startup Investment Decision Support: Application of Venture Capital Scorecards Using Machine Learning Approaches." Systems 9, no. 3: 55.
Musculoskeletal disorders affect the locomotor system and are the leading contributor to disability worldwide. Patients suffer chronic pain and limitations in mobility, dexterity, and functional ability. Musculoskeletal (bone) X-ray is an essential tool in diagnosing the abnormalities. In recent years, deep learning algorithms have increasingly been applied in musculoskeletal radiology and have produced remarkable results. In our study, we introduce a new calibrated ensemble of deep learners for the task of identifying abnormal musculoskeletal radiographs. Our model leverages the strengths of three baseline deep neural networks (ConvNet, ResNet, and DenseNet), which are typically employed either directly or as the backbone architecture in the existing deep learning-based approaches in this domain. Experimental results based on the public MURA dataset demonstrate that our proposed model outperforms three individual models and a traditional ensemble learner, achieving an overall performance of (AUC: 0.93, Accuracy: 0.87, Precision: 0.93, Recall: 0.81, Cohen’s kappa: 0.74). The model also outperforms expert radiologists in three out of the seven upper extremity anatomical regions with a leading performance of (AUC: 0.97, Accuracy: 0.93, Precision: 0.90, Recall:0.97, Cohen’s kappa: 0.85) in the humerus region. We further apply the class activation map technique to highlight the areas essential to our model’s decision-making process. Given that the best radiologist performance is between 0.73 and 0.78 in Cohen’s kappa statistic, our study provides convincing results supporting the utility of a calibrated ensemble approach for assessing abnormalities in musculoskeletal X-rays.
Minliang He; Xuming Wang; Yijun Zhao. A calibrated deep learning ensemble for abnormality detection in musculoskeletal radiographs. Scientific Reports 2021, 11, 1 -11.
AMA StyleMinliang He, Xuming Wang, Yijun Zhao. A calibrated deep learning ensemble for abnormality detection in musculoskeletal radiographs. Scientific Reports. 2021; 11 (1):1-11.
Chicago/Turabian StyleMinliang He; Xuming Wang; Yijun Zhao. 2021. "A calibrated deep learning ensemble for abnormality detection in musculoskeletal radiographs." Scientific Reports 11, no. 1: 1-11.