# Towards Unlocking the Hidden Potentials of the Data-Centric AI Paradigm in the Modern Era
## 5 Insight to When to Amalgamate DC-AI with MC-AI
### Limited Availability of Representative Data  
In many real-world cases, high-quality data may not be available, and the acquisition of new data can be difficult owing to constrained budgets. Therefore, the doubling rule for training data employed in MC-AI may not be possible. In this context, DC-AI is invaluable for improving the existing data, and compensating for a deficiency in giant datasets. Besides the above-cited aspects/problems, the integration of DC-AI with MC-AI is necessary and can advance AI technology from multiple perspectives. MC-AI has already been well-investigated in the recent past. Hence, now is the right time to put more effort into DC-AI to improve the overall status of AI technology. Case Study (Proof of Concept Example) to Evaluate the Effects of the Key Parameters of DC-AI on Performance  
The DC-AI paradigm has already achieved breakthroughs in many AI applications such as reducing the time from the implementation to the deployment of AI models, training AI models with fewer data, accelerating the convergence of AI models, reducing the overall complexity of AI models, reducing the cost of AI technology by exploiting the code of already-developed models, and extending AI applications [59, 60, 61, 62, 63]. The umbrella of DC-AI applications and techniques is constantly expanding, and therefore, the adoption of AI technology is increasing worldwide. In some cases, the existing AI applications are being modified to encompass the concepts of DC-AI to increase their reliability and efficiency in practical scenarios. Below, we demonstrate an empirical case study (proof of concept example) concerning DC-AI, which can address the technical shortcomings of traditional AI models by improving data quality. Enhancing the Learning Ability and Generalization Power of ML Algorithms in Safety-Critical Applications (e.g., Medical Scenario)  
In this case study, we examine the impact of DC-AI on the learning ability and generalization power of ML algorithms in safety-critical applications, specifically medical diagnosis. To analyze the effects of the key parameters of the DC-AI paradigm on the performance, we chose the stroke prediction dataset [64]. This dataset is publicly available and has been widely used in the AI community for predicting the possibility of stroke [65]. It encompasses a variety of features and has stroke as the target class. The detailed information of the predictors in this dataset is given as follows: Gender (C), Hypertension (N), Ever-married (C), Work type (C), Residence type (C), Smoking status (C), Age (N), Heart disease (N), Glucose level (N), BMI (N), Patient id (N). The target class is stroke (N), the probability having values 0 and 1. We denote both types of features (e.g., categorical and numerical) by C and N, respectively. In this dataset, the cardinality of the target class (e.g., stroke) is two, and the frequency of each value is different. Figure 6 highlights the frequency values of each category. Referring to Figure 6, it can be observed that there exists a very high imbalance between stroke (probability value = 1) and no stroke (probability value = 0). In the realistic scenario, the frequency of both stroke probability value = 1 and stroke probability value = 0 should be close enough to ensure the balanced learning/training of AI models. In the initial phase, we applied the MC-AI approach for computing the accuracy value using the equation below. \[\mathcal{A}=\frac{T_{p}+T_{n}}{T_{p}+F_{p}+T_{n}+F_{n}} \tag{1}\]  
where \(T_{p},T_{n},F_{p},F_{n}\) denote the true positives, true negatives, false positives, and false negatives, respectively. We built the three ML models from the stroke prediction dataset without improving its quality, just like traditional AI system development. We applied three different settings:  
* In setting I, we fixed the parameters of the ML models, but varied the amount of test and training data. For the sake of simplicity, we name this setting as \(\alpha\).