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Heart disease is one of the worst life-threatening conditions. Correct and early diagnosis of this disease is crucial for saving patients’ life and avoiding other complications. On the other hand, keeping the patient’s data, diagnosis process, and treatment plan secured is equally important to the defactomedical procedure. This research proposes a system that is consisting of two phases: security provision and patients’ condition diagnosis. Typically, the first phase exercises a security protocol, called three-pass protocol, to ensure that the people who can access the patient's information are authorized. In order to obtain a high accuracy level in the diagnosis process, artificial intelligence with machine learning methods are employed in the later phase. The proposed system relies on a data set which includes a number of vital indicators, by which the patient's status can be classified as having heart disease or not. The KNN algorithm and the random forest tree algorithm are applied to carry out the classification task. The accuracy scale results reveals that the randomforest tree algorithm (99%) gave higher accuracy than KNN (97%).

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State Machine (ASM) method is a formal specification and modeling technique that allows us to specify computational systems at the required abstraction level and facilitates formal analysis and verification. System Theoretic Process Analysis (STPA) is a semiformal hazard analysis method that aims to identify safety requirements emerging from the analysis of potential interactions among components and inadequate control in the system's design. In this paper, we combine these two techniques to develop a methodology capturing both the formal representation of ASM with the ability to generate safety properties from the STPA hazard analysis. This has the advantages of verifying the STPA requirements in a formal way, and giving insights for the improvement of the ASM specification, depending on these requirements. We illustrate our methodology by applying it to an insulin pump control system case study, showing what safety issues it highlights.

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