## 1 Introduction  
Despite the growing usage of machine learning in medical systems, there are multiple inconveniences that prevent its widespread usage. Different types of approaches may not perform as accurately as desired in the field of healthcare, or perform better or worse on different types of data [1, 2, 3, 4]. In addition to that, existing solutions cannot always be applied efficiently due to the lack of common data schema for healthcare organizations to rely on [5, 6, 7]. This requires conversion between different data formats, and the rarity of solutions that automate those things make it a serious obstacle. One of the more frequently used formats is plain text, in the form of patient notes, making it even more difficult for AI (Artificial Intelligence) and automatic health systems to make use of existing data without it being transformed manually. As a result, the task of automating the conversion of this data still remains important to this day [8]. Different possibilities of handling this task are still being explored, but the results of this research are both scary and largely remain hidden from the public eye (with larger companies and organizations preferring not to share their patents and studies). These factors cause the question of parsing patient notes into more computer-friendly formats to stay open for now. Natural language processing might be a possible solution to the problem of heterogeneity if it could be forced to analyse column labels in the data structure itself. Assuming that familiar columns are named similarly, we could potentially detect features that are dependent on each other. As a continuation of said idea, language processing could help detect related features that are expressed in different units and help convert them [9; 10]. Unit conversion within the context of heterogeneous datasets is yet another issue that requires exploration. While the extracted labels may contain clues as to what units were used for the individual dataset, this is not always the case. Due to this, this paper proposes a similarity-based approach towards unit conversion in features, where a feature conversion rate for new data is being approximated based on samples from existing data that are most similar in regard to features that we know to be equivalent [11; 12]. Another problem with regard to medical data is that different diagnoses is the multi-collinearity, which can distort the end result due to the classifiers receiving the same data twice. Difficulties may arise when merging datasets, with different default values being assigned to the samples in missing fields, making the features look more different than they are as a result of an assumption, which gives us a problem of finding default values that will distort the data the least [13; 14; 15]. The main contribution of the paper relates to the application of selected machine learning methods in healthcare for the transformation of medical data regarding the cardiovascular problem, with particular emphasis on the heterogeneity of medical data sources, which were addressed in [10; 16; 17; 18; 19; 20; 21] in a manner different from the approach described in our approach, i.e., as follows:  
1. Assesses the efficiency of various approaches of machine learning when applied in healthcare field, with particular emphasis on the heterogeneity of medical data sources;
2. Uses existing Machine Learning classification algorithms with NLP for the medical data transformation on the problem of cardiovascular in healthcare systems;
3. Proposes the solutions to the problem of plain text data transformation and data heterogeneity with the help of natural language processing. The paper is structured as follows: Section 2 presents the related work on machine learning for medical system. Section 3 gives the outline of health data formats in medical systems. Section 4 features the proposal in data transformation for tables and text to resolve incompatibilities between medical formats.