## 3 Materials and Methods
### Server Architecture
#### 3.3.2 Web Application: Exposure Data and Alerts
\begin{table}
\begin{tabular}{c c c c} \hline
**Agents** & **Pre-Diagnosed Diseases** & **Related Occupational Diseases** & **Symptons** \\ \hline  & Diabetes type 2 & & Difficulty understanding conversation in situations with background noise \\ Noise [(62,7,73)] & Hearing disorders & Hearing loss & Feeling that the same are plugged up \\  & Hypertension & Hypertension & Speech or other sounds modelled after exposure to loud noise \\  & Smoker & & Transparent tinnitus \\ \hline  & & & Dehydration \\  & & Skin lesions & \\  & & Heat stroke & \\  & Eye disease & Burns & Chest pain \\ UV radiation [(74, 75, 76)] & Heart disease & Pos Skin cancer & Dyspnea (shortness of breath) \\  & & Hypertension & History of resection of skin lesions \\  & Skin diseases & Active inflammatory reactions & Skin lesions \\  & & Increased risk of cataracts & Skin rash \\  & & Suppression of the immune system (lavves the & \\  & & occurrence of infections and cancer) \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._It is important to highlight that regarding pre-diagnosed diseases, the proposed approach is based on the following: a certain previously diagnosed disease might be caused by various factors, such as genetics or agents related to professional activities or others. If a worker who has that disease is often exposed to agents that are known to aggravate such conditions, his or her health status tends to worsen. In addition, it cannot be concluded that a disease was caused by exposure to certain agents unless the occupational physician has supporting documentation/tests available. This aspect was considered in the present study. For example, according to [77], family history, air pollution, smoking, and occupational exposures are among the factors that play an important role in the development of asthma. For such reasons, the proposed web application considers all of the agents related to asthma (cold, dust, and high and low humidity) for a worker who has this condition and generates an alert to reduce or avoid exposure to these agents. Concerning the symptoms that may be reported by workers during registration or information updating, they may be motivated by diseases that cannot be related to their professional activities and that have no severity. On the other hand, a reported symptom may be one of the first signs of a serious disease related to the activities carried out by the worker in the company. It is essential to highlight that this type of assessment can only be carried out by a physician, as well as requests for additional tests that can confirm or rule out suspicions. In other words, the objective of the proposed system is that alerts issued preventively can assist OSH professionals/physicians in decision making to define, for example, when it is necessary to carry out further investigation. Aside, OSH professionals may use the information provided by the system as input to guide personalized health-care routines and create or improve internal health awareness campaigns. For example, OSH professionals may advise managers and supervisors to plan workers' activities aiming to minimize exposure to certain agents. The recommendation system to be implemented is composed of two parts: the machine learning module that will analyze the exposure data provided by each monitoring device and classify the data as risky or not, and the already implemented module that performs the verification of the health conditions as described above. This approach is expected to improve the alerts.