Journal of Biometrics & Biostatistics

SNE methods are a set of 9 to 10 interconnected algorithms that map high-dimensional data into low-dimensional space while minimizing loss of information. Each step in this process is important for producing high-quality maps. Cense′™ mapping not only enhances many of the steps in this process but also fundamentally changes the underlying mathematics to produce high-quality maps. The key mathematical enhancement is to leverage the Cauchy distribution for creating both high-dimensional and lowdimensional similarity matrices. This simple change eliminates the necessity of using perplexity and entropy and results in maps that optimally separate clusters defined in high-dimensional space. It also eliminates the loss of cluster resolution commonly seen with t-SNE with higher numbers of events. There is just one free parameter for Cen-se′ mapping, and that parameter rarely needs to change. Other enhancements include a relatively low memory footprint, highly threaded implementation, and a final classification step that can process millions of events in seconds. When the Cen-se′ mapping system is integrated with probability state modeling, the clusters of events are positioned in a reproducible manner and are colored, labeled, and enumerated automatically. We provide a step-by-step, simple example that describes how the Cen-se′ method works and differs from the t-SNE method. We present data from several experiments to compare the two mapping strategies on high-dimensional mass cytometry data. We provide a section on information theory to explain how the steepest gradient equations were formulated and how they control the movement of the low-dimensional points as the system renders the map Since existing implementations of the t-SNE algorithm can easily be modified with many of these enhancements, this work should result in more effective use of this very exciting and far-reaching new technology.

A statistical study has been conducted to identify the risk factors of heart attack. The study design used in this research is an observational cross sectional. A semi structured questionnaire was designed and surveyed consisting of 25 questions which were filled from 246 patients from two hospitals ‘Gulab Devi’ and ‘Jinnah Hosptial’ Lahore, Pakistan. Respondents were asked questions regarding some of the possible reasons that may cause heart attack. Out of 246 patients, 123 were cases (people who had a heart attack) and remaining 123 were control (people who only had chest pain). We took 123 patients in each group because we needed comparison. Spss and R SOFTWARE were used to determine results of this research. By using univariate, bivariate and multivariate analysis it was observed that the significant factors from model are diabetes blood pressure, sweating, heart attack before, age, severity of pain, medication and pressure of the work.