With too little physical activity, stress and other factors, an unhealthy diet significantly affects a large number of people contracting chronic and other current diseases (e.g., diabetes, cardiovascular diseases, neurological disorders or metabolic syndromes), as in , . Mobile and web technologies enable entry and collection of data on people’s condition and needs, while portable devices and sensors can be used as a source of valuable data that can facilitate planning a healthier diet adjusted to the diagnosis, condition and symptoms of a patient or a user with a certain level of risk of developing a disease , . The analysis of the input and collected data should enable preparation of a nutrition profile of the user, planning, adjusting and monitoring eating habits and nutritional options, as in , . On the other hand, the cloud provides infrastructure, computing, development/platform and analytical capabilities for developing a balanced diet system, as in , . At day, week, and month levels, your start-up company should be able to suggest healthy food, give recommendations for its consumption, find places that sell healthy food, and avoid or warn of places selling unhealthy food. As in , , based on the principle of machine learning from the data, your company should also enable self-monitoring of patients’ condition and follow-up by a physician and/or a nutritionist.
 A. Abbas, et al. “Personalized healthcare cloud services for disease risk assessment and wellness management using social media,” Pervasive and Mobile Computing, vol. 28, pp. 81-99, June 2016.
 V. Apaolaza, et al. “Eat organic – Feel good? The relationship between organic food consumption, health concern and subjective wellbeing,” Food Quality and Preference, vol. 63, pp. 51.62, Jan. 2018.
 V. Kumari Yeruva, S. Junaid, Y. Lee. “Exploring social contextual influences on healthy eating using big data analytics,” in 2017 IEEE Int. Conf. on Bioinformatics and Biomedicine (BIBM), Kansas City, MO, USA, 2017, pp. 1507-1514.
 C.-H. Wu, C.-H. Hung, J.-C. Ke. Analysis techniques of food nutrient data,” in Proc. ASE Big Data & Social Informatics 2015 (ASE BD&SI 2015), Kaohsiung, Taiwan, 2015, Article No. 11.
F.M. Shiddieq, R. Kastaman, I. Ardiansah. “Development of people food consumption patterns information system based on web mobile application,” 2015 3rd
Int. Conf. Adaptive and Intelligent Agroindustry (ICAIA), 3-4 Aug. 2015, pp. 267-273.
 Big data and analytics, [Online]. Available: https://azure.microsoft.com/en-us/solutions/big-data
 M.M. Al-Jefri, et al. Using machine learning for automatic identification of evidence-based health information on the web,”, in 2017 Int. Conf. on Digital Health (DH ’17), London, UK, 2017, pp. 167-174.
 D. Ntalaperas, et al. “DISYS: an intelligent system for personalized nutritional recommendations in restaurants,” in 19th
Panhellenic Conf. on Informatics, Athens, Greece, 2015, pp 382-387.
 S. Wolfert, L. Ge, C. Verdouw, M.-J. Bogaardt. Big data in smart farming – a review,” Agricultural Systems, vol. 153 pp. 69-80, May 2017.