## 2 Literature Review
This approach was justified by the efficacy of the method and substantiated using a common cardiovascular data set. Data charting was also used in order to determine the precision with which they found abnormalities in their experimental research. The majority of the aforementioned studies, which produced good results in heart disease prediction, employed supervised and deep learning algorithms, both of which need extensive labelled data [16; 17; 18; 19; 20; 21; 22]. Several researchers have used collaborative clustering, which significantly improves the clustering outcomes [23; 24; 25; 26; 27]. All the above algorithms have the same limitations. Firstly, they rely on prototypes based on fixed parameters and algorithms from the same family, and secondly, they find identical clusters. In one study [28], a horizontal collaborative clustering method is used, referred to as SAMARA. However, this uses only hard clustering as it does not deal with prototypes and is not limited to a specific number of clusters. The goal is to modify the clustering results for the same data iteratively and collaboratively, thereby lowering their diversity and facilitating the discovery of a consensus solution. After completing the local step, the clusters are mapped to different clustering algorithms using a probabilistic confusion matrix. A new method [5] was proposed that allows the enhancement of the clustering process by exchanging information between several local results. This addresses the limitations of previous techniques that depended on the algorithms being both homogeneous and from the same family. The data are split using a horizontal collaborative [4; 5] method, either into subsets that represent the same data on different features, or into the same data but searching for a different number of clusters or a mixture between them. Several heterogeneous algorithms are used, including Self-Organizing Mapping (SOM) [23; 25], the Generative Topographic Mapping Algorithm (GTM) [26; 27] and Expectation Maximisation (EM) [4], which can be applied to different probability distributions. These probability distributions include the Gaussian mixture distribution, the Dirichlet distribution [29] and the Bernoulli mixture model (BMM) [30]. The approach based on the SAMARA modification [4] avoids the limitations found in previous studies by having the requisite for identical prototypes amongst various collaborators and by employing an abundance of clustering algorithms from other families. This is based on an estimation process that operates on subsequent distributions known as composite functions. These techniques handle data with the same feature data type, e.g., numeric or imaging data. One of the challenges in the current proposed approach is handling mixed data. Most clustering algorithms do not have the ability to deal with mixed data. This is because each clustering algorithm has a specific distance measure. It may be specialised in dealing with quantitative data or dealing with qualitative data. For example, k-means depends on the Euclidean distance measure for quantitative data, and k-modes uses a categorical measure to calculate distance. Therefore, there is no single algorithm that works with mixed data. The current study investigates a different approach in order to optimise the clustering algorithm results. This strategy has large-scale advantages which include the ability to apply collaborative clustering techniques to medical specifications or to alternative domains. Additionally, there is the option to predetermine the cluster number [31]. In this study, two clusters will be predetermined in other to predict whether or not an individual has a CVD.