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Corruption poses an enormous challenge to all domestic and foreign enterprises operating in Russia (Bremmer 2020). The Association of Chartered Certified Accountants (ACCA) estimated the Russian “shadow” economy to be around 40% of nominal GDP. This makes it the fourth largest in the world, behind Azerbaijan, Nigeria, and Ukraine. Transparency International (TI), in its corruption perception index, ranked Russia as low as the 138 least corrupt country in 2018 out of the 180 countries in the world. This is much higher than other major developing economies, the worst amongst BRICS member states (Table 23.1, Fig. 23.1).

Heart sounds recognition is essential for heart defects diagnosis. Diagnosis of structural heart defects is not always possible using the contemporary stethoscope and need further assessment using high cost devices such as X-ray, electrocardiogram (ECG), echocardiography (ECHO) and computed tomography (CT)). Automatic computer assisted auscultation may be used as a clinical decision support tool. In an attempt to develop an automatic computer aided diagnostic modality for heart conditions that is sensitive, specific, non-invasive we created two automatic computer cardiac auscultation (ACCA) models that provide heart sound analysis and we aimed to improve the sensitivity and correct classification rate (CCR) of recognition of heart sounds, thus we developed model A ACCA recognition system (machine learning (interpreter independent)) and model B ACCA recognition system (machine learning and interpreter dependent visual analysis). We used machine learning based on mel frequency cepestral coefficients as a feature and Hidden Markov Model (HMM) as a classifier. We performed visual analysis based on phonocardiography (PCG) and spectrogram image. Model A ACCA demonstrated 97% CCR, 99.2% sensitivity and specificity 100% and model B ACCA demonstrated 98.2% CCR and 99.2% sensitivity and specificity 100%. ACCA models A and B allow reliable interpretation of recognised sounds to detect underlying structural defects.