# AI-Based Prediction of Myocardial Infarction Risk as an Element of Preventive Medicine
## 4 Discussion  
Recent reviews of the application of machine learning to myocardial infarction have attempted to answer at least some the following important issues:  
* identification of knowledge gaps [15]
* methods for standardizing diagnostic imaging results through reference image exchange between observers and artificial intelligence thus enabling accurate measurements [16]. * the use of ML to accelerate diagnostic and therapeutic procedures allowing an earlier discharge of the patient from the ward or an early start of rehabilitation [1]. * the potential to use machine learning methods to create predictive models to improve assessment, including multimarker-based methods in the assessment of complex, multifactorial diseases [17; 18]. * new approaches in cardiac anaesthesia [15]. * creation and clinical application of hybrid approaches [1]. The prevalence of cardiovascular diseases worldwide, especially in people over 65 years of age, will increase significantly with the ageing of the population, becoming a  
Figure 13: Feature importance. major socioeconomic problem from the increasing risk of heart attack and stroke, amputation and death. Counteraction is hampered by the fact that, despite its poor prognosis, the above-mentioned group of patients is rarely identified in the early stages of the disease, let alone treated by preventive medicine. This implies the need to search for reliable biomarkers which could contribute to targeted personalized diagnostics, prevention and treatment at very early stages of the disease [17]. Machine learning (ML), increasingly being used to complement conventional statistical modelling (CSM), is rarely compared in different prognosis studies. Therefore, further research is needed that maintains clinical quality standards for prognosis studies [18]. Sixteen years ago, the European EPI-MEDICS project (2001-2004) showed an affordable mobile personal ECG monitor for the early detection of arrhythmia and cardiac ischemia [19]. Despite quick development of these early devices, the number of AI-based studies on MI still remains insufficient. Automation is important in reducing unnecessary hospital admissions: less than 20% of patients screened during emergency hospital admissions have an acute coronary syndrome (ACS) [20]. Advances in diagnostics have already increased the efficiency of care pathways. In two-thirds of patients, acute myocardial infarction can be ruled out on the basis of a sample taken after 1 h [20]. The Troponin-only Manchester Acute Coronary Syndromes (T-MACS) model can be used for ACS prediction using AI [20]. New medical devices enable earlier detection and can analyse large datasets providing pre- and post-operative predictions [21]. ML and deep learning (DL) techniques can lead to more accurate CVD/stroke risk stratification [22]. AI, mainly ANNs, can already extract features with high predictive power from ECGs [23]. Several ECG parameters with prognostic value have already been proposed [24]. Essential application areas for AI in cardiovascular imaging include: patient triage, and clinical decision support [25]. AI can help support the identification of whether destructive changes are developing in healthy myocardium after an ischemic event, which could subsequently develop into fibrous scar tissue. AI can analyse phenomena occurring in ischemic myocardium [26]. For arrhythmias unrelated to atrial fibrillation, therapy is supported, but adequate accuracy and validation of the clinical pathways are required [27]. Artificial intelligence-based cIMT/PA segmentation methods have been developed to monitor the risk of CVD/stroke [28]. Some works have proposed similar ideas but for other diseases: dementia classification using MRI imaging and clinical data [29], and autism spectrum disorder [30]. An important difference is that they looked at neurodegenerative (dementia) or congenital (ASD) conditions, rather than the risk/prediction of the condition in healthy individuals.