electricsheepafrica/african-malaria-dataset

非洲疟疾合成数据集，使用文献支持的随机概率模型生成，用于疟疾检测和严重程度预测。数据集包含了年龄、性别、居住地、季节、是否使用蚊帐、疟疾状态、寄生虫密度、恶性疟原虫种类、血红蛋白水平、贫血状态、发热天数、是否发热、是否有寒战、是否有头痛、是否有呕吐、是否有腹泻、是否有虚弱、是否患有重症疟疾、是否患有脑型疟疾、是否有呼吸窘迫、是否有休克、是否有急性肾损伤、结果和疟疾概率分数等特征。数据集分为训练集、验证集和测试集，可用于开发机器学习模型，以支持在资源有限的非洲地区进行疟疾筛查、寄生虫密度估计和重症病例识别。

The African Malaria Synthetic Dataset is a set of synthetic data generated using literature-informed probabilistic modeling for malaria detection and severity prediction in Sub-Saharan Africa. The dataset includes features such as age, sex, residence, season, use of mosquito nets, malaria status, parasitemia level, Plasmodium species, hemoglobin level, anemia status, fever duration, symptoms, severe complications, outcomes, and malaria probability scores. The dataset is split into training, validation, and test sets and is designed to support the development of machine learning models for malaria screening, parasitemia estimation, and severe case identification in resource-limited African healthcare settings.