## 7 Conclusions  
Smartness, which underpins smart cities and societies, is defined by our ability to engage with our environments, analyze them, and make decisions, all in a timely manner. Healthcare is the prime candidate needing the transformative capability of this smartness due to reasons including healthcare spending reaching a significant proportion of national GDPs (around one-fifth of the GDP in the US), an aging population, gross inefficiencies, and bad eating habits around the world, giving rise to the prevalence of lifelong diseases. With half of the world population connected to social networks, social media provides a vital solution for a ubiquitous and timely engagement among healthcare stakeholders. Twitter is one of the most popular social media platforms today. 500 million tweets are sent every day. Saudi Arabia has the fifth largest number of Twitter users in the world. Our focus in this research has been 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 provided an extensive review of the relevant literature and identified two major challenges: first, the rudimentary level of the existing research (in terms of scope, functionalities, and usability) on Twitter data analytics in healthcare in English, and particularly in other languages, including Arabic; second, the scalability and interoperability of the analytics tools for healthcare, such as the management, integration, and distributed computations of big data. We proposed Sehaa, a big data analytics tool for healthcare in Saudi Arabia using Twitter data in Arabic. Sehaa used Naive Bayes and Logistic Regression and multiple feature extraction methods to detect various diseases in Saudi Arabia. Sehaa was able to successfully detect various diseases. The top five diseases in Saudi Arabia in terms of the actual afflicted cases are dermal diseases, heart diseases, hypertension, cancer, and diabetes. Riyadh and Jeddah need to do more in creating awareness about the top diseases. Taif is the healthiest city in the KSA in terms of the detected diseases and awareness activities. Sehaa is developed over Apache Spark, allowing true scalability. The results were evaluated using the well-known numerical criteria Accuracy and F1-Score, obtaining 83.9% and 86.7% scores for the two classification stages. The Sehaa results were validated against externally available statistics and shown to have a good correlation with them. For example, heart disease was found to be one of the top causes of death in Saudi Arabia, as reported in external sources, and this was in agreement with Sehaa, which detected heart diseases among the top five diseases in the country. Taif was shown to have low disease occurrence in external media and this was also in agreement with the results obtained through Sehaa. Sehaa is an excellent example of integrating artificial intelligence (AI), distributed big data computing, and human cognition, brought together as a convenient tool for the betterment of public health and the economy. The system methodology and design are generic and can be extended to other countries in the Arab world as well as globally. Our focus in this work is on Saudi Arabia and therefore the tool currently works with tweets only in the Arabic language (it can be used in other Arabic speaking countries, such as UAE, Kuwait, and Egypt). Potential users of this tool are hospitals and other healthcare organizations, ministries of health, pharmaceutical companies, and other healthcare stakeholders. This study is the first of its kind in Saudi Arabia using Apache Spark and tweets in the Arabic language. Sehaa is an important step in developing data analytics tools for Twitter (and other social media) in Arabic. The use of a scalable distributed computing platform for big data (Apache Spark) in this paper is also an important step in the right direction.