As of October 2019, Statista reports that Saudi Arabia has the fifth largest number of Twitter users in the world (10.09 million, roughly one third of its population), only after the US, Japan, Russia, and the UK [7]. Moreover, Saudi Arabia has the highest proportion of Internet users who are active on Twitter. 80% of the Twitter users access it through mobile phones, that allowing connecting to people anywhere anytime, providing access to rich spatio-temporal information [9]. Our research focuses on the use of Twitter media for healthcare in Saudi Arabia, with the aim to develop technologies that provide enhanced healthcare in the country. We have performed a review of the relevant literature (see Section 3), and it reveals that there are two major challenges in this quest. First, the current state of research on the topic is in its infancy, both in terms of the scope of the works [10] as well as the investigation into the methods for healthcare analytics, including that of machine learning methods [11]. Several works exist on the analysis of tweets in the English language; however, much more is needed for developing robust analytics methods, tool functionalities, and usability. The state of research in languages other than English is even more rudimentary. For the Arabic language, while some works (not in healthcare) are available in Modern Standard Arabic (MSA) [12; 13], the works on the Arabic dialects are very limited in numbers and scope [10; 14]. Moreover, we have found only three works in Arabic specific to healthcare [15; 16; 17], but these are limited in scope, depth,and/or functionalities. Specifically, there is no known work in data analytics of the Saudi Arabic dialect in healthcare. The second challenge relates to the fact that the scalability and interoperability of the Twitter data analytics tools for healthcare have not been considered. The challenges in this respect include management, integration, and distributed computation of data, including the difficulties related to managing the 4V characteristics of big data, i.e., volume, velocity, variety, and veracity of data. There have been some works on the use of big data platforms in Twitter data analysis in various application domains [18; 19; 20; 21; 22; 23]. However, to the best of our knowledge, no work exists that uses big data technologies for data analytics in healthcare using tweets in the Arabic language. This paper proposes Sehaa (an Arabic word meaning health), a big data analytics system for healthcare. Sehaa is composed of four main modules. The Data Collection module captures and stores public tweet messages from Saudi Arabia using a Twitter streaming API (application programming interfaces) according to a set of predefined parameters. Social media contents are unstructured and contain many errors (e.g., the veracity of big data). The Pre-Processing module cleans and manually labels the data to prepare it for the actual learning and classification stages. The Classification Module comprises six classification models (classifiers) that are used in two classification stages. The classification methods used include Naive Bayes (NB) and Logistic Regression (LR), in combination with four feature extraction methods: BiGram, TriGram, HashingTF, and CountVectorizer. The Validation Module evaluates the classifiers' performance against two widely used numerical evaluation criteria, F1-Score and Accuracy. The results are visualized and validated against external sources such as national statistics, research reports, and news media. The Sehaa tool is used in this paper to detect symptoms, diseases, and medications in Saudi Arabia. The results are collected and analyzed in terms of the detected diseases and the level of awareness generated in five major cities in the country--Riyadh, Jeddah, Dammam, Makkah, and Taif. Sehaa found that the top five diseases in Saudi Arabia in terms of the actual afflicted cases are dermal diseases, heart diseases, hypertension, cancer, and diabetes.