# Impact of Dataset Size on Classification Performance:
## 5 Conclusions  
Recent years have witnessed an increased interest in modern healthcare services, such as automated diagnosis and personalized medicine. However, the success of such services is eminently dependent on the availability of datasets. Collecting medical data may face many challenges such as patients' privacy and lack of data for rare conditions. This work aims to investigate the impact of dataset size on the performance of six widely-used supervised machine learning models in the medical domain. For this purpose, we carried out extensive experiments on six classification models including SVM, NN, DT, RF, AB, and NB using twenty medical UCI datasets [24]. We further implemented three dataset size reduction scenarios on two large datasets, resulting in three small subsets. We then analyzed the change in performance of the models as a response to the reduction of dataset size with respect to accuracy, precision, recall, f-score, specificity, and AUC. Statistical tests are used to assess the statistical significance of the differences in performances in different scenarios. Several interesting conclusions can be made. First, the overall performance of classifiers depends on the extent to which a dataset represents the original distribution rather than its size. Second, the most robust model for limited medical data is AB and NB, followed by SVM, and then RF and NN, while the least robust model is DT. Third, a robust machine learning model to limited dataset does not necessarily imply that it provides the best performance compared to other models. Our results are in agreement with previous studies [2]. A natural progression of this research would be to investigate the minimum dataset size that each classifier needs in order to maximize its performance. **Author Contributions:** Conceptualization, H.A.-B. and H.K. ; Data curation, A.B.D. and N.A. ; Formal analysis, D.A., H.A.-B., and A.S.; Funding acquisition, H.K. ; Investigation, A.A.; Methodology, D.A., H.A.-B. and H.K. ; Software, A.B.D. and N.A. ; Supervision, D.A. and H.K. ; Validation, A.A., A.S., and H.K. ; Visualization, A.A., A.S., and A.A.E. ; Writing--original draft, D.A., H.A.-B., A.B.D. and N.A. ; Writing--review and editing, A.A. and H.K. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: [[[https://archive.ics.uci.edu/ml/datasets.php](https://archive.ics.uci.edu/ml/datasets.php)]([https://archive.ics.uci.edu/ml/datasets.php](https://archive.ics.uci.edu/ml/datasets.php))]. **Acknowledgments:** This research was supported by a grant from Researchers Supporting Unit, Project number (RSP-2020/204), King Saud University, Riyadh, Saudi Arabia. **Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. **Appendix A**  
**Table A1. ** Precision of classifiers trained on small datasets. \begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{**Classifiers**} & & & & \\ \hline
**Datasets** & **AB** & **RF** & **NN** & **DT** & **NB** & **SVM** & **Avg. ** & **Std. Dev. ** \\ \hline DS1 & 99.00\% & 99.00\% & 99.00\% & 99.00\% & 99.00\% & 99.00\% & 99.00\% & 0.000 \\ \hline DS2 & 83.10\% & 79.20\% & 82.40\% & 83.60\% & 82.80\%