## 6 Prevalent Criteria
### Datasets Commonly Used in ML Implementation for ISs in Multi-Objective Healthcare Management  
The value of broadly leveraged datasets in ML implementation for ISs in multi-objective healthcare management cannot be neglected. These datasets are the basis for training, validating, and fine-tuning ML frameworks to build precise and reliable forecasting. Access to wide and representative healthcare datasets is important for assuring model generalizability over an extensive domain of patient populations and circumstances [4]. Common datasets enable the generation of advanced algorithms that may aid healthcare providers make better-informed decisions about diagnosis, treatment plans, and total patient care. Also, these datasets serve a key role in promoting contribution and benchmarking in the healthcare and ML communities, enabling the establishment of standardized and interoperable solutions that may benefit the total healthcare settings [99].  
Several popular datasets are frequently utilized in ML implementations for decision-making in HISs. Some notable examples include:  
* **MIMIC-III (Medical Information Mart for Intensive Care III):** This is a thorough dataset that includes the de-identified health data of patients admitted to important care units. It involves medical notes, demographics, laboratory test outcomes, and other information, making it remarkable for predictive modeling and medical decision support [100];
* **UCI ML Repository--Healthcare Datasets:** The UCI repository hosts a variety of healthcare datasets, such as the heart disease dataset and the diabetes dataset. These datasets envelop a scope of health situations and are usually leveraged for classification tasks in ML investigation [101];
* **The Cancer Genome Atlas (TCGA) offers genetic and clinical data for different cancer types, allowing researchers to create ML models for diagnosis, prognosis, and therapy response prediction [102];
* **PhysioNet Database:** It generates databases for physiological signals, containing ECG and blood pressure data. These datasets are applied to build algorithms for monitoring and detecting heart disease [103];
* **Diabetes Dataset (Pima Indian Diabetes):** This dataset is prevalently utilized for forecasting the onset of diabetes. It includes information on patient demographics, medical history, and diagnostic measures [104];
* **Image Datasets (e.g., Chest X-rays, MRI, CT scans):** Diverse image datasets, like the ChestX-ray14 dataset or datasets from the Kaggle Data Science Bowl, are suitable for improving ML systems for medical image assessment, enabling tasks like disease recognition and classification [105];
* **CMS Medicare Data:** The Centers for Medicare and Medicaid Services (CMS) publishes databases on healthcare uses and spending, generating perspectives into healthcare patterns [106].  
It is vital to remember that the dataset used is determined by the individual study or application aims. Researchers and practitioners must ensure that the datasets utilized are varied, representative, and consistent with the goals of the ML implementation for decision-making in HISs. Furthermore, safeguarding patient privacy and following ethical rules are critical concerns when working with healthcare data.