Diagnosing Heart Diseases For Type 2 Diabetic Patients By Cascading The Data Mining Techniques

Motivated by the world-wide increasing mortality of heart disease patients each year, researchers have been using data mining techniques to help health care professionals in the diagnosis of heart disease. Heart disease is the leading cause of death in the world over the past 10 years. Researchers have been using several data mining techniques to help health care professionals in the diagnosis of heart disease. To review the primary prevention studies that focused on the development, validation and impact assessment of a heart disease risk model, scores or rules that can be applied to patients with type 2 diabetes. Efficient predictive modeling is required for medical researchers and practitioners. Attribute values measurement using entropy and information gain parameters. This study proposes Hybrid type 2 diabetes Prediction Model which uses Improved Fuzzy C Means (IFCM) clustering algorithm aimed at validating chosen class label of given data in which incorrectly classified instances are removed and. pattern extracted from original data. Support Vector Machine (SVM) algorithm is used to build the final classifier model by using the k-fold cross-validation method. The aim of this paper is to highlight all the techniques and risk factors that are considered for diagnosis of heart disease. This paper will provide a roadmap for researchers seeking to understand existing automated diagnosis of heart disease

Aberrant TGFβ/BMP signalling in connective tissue disease associated pulmonary hypertension

Up to 10 percent of systemic sclerosis (SSc) patients develop pulmonary arterial hypertension (PAH). This risk persists throughout the disease and is time dependent, suggesting that SSc operates as a susceptibility factor. Outcomes for SSc-PAH patients remain poor compared with heritable (HPAH) or idiopathic (HPAH) forms, despite clinical and pathological similarities. Whereas susceptibility in HPAH and HPAH is strongly associated with gene mutations that lead to reduced expression of functional bone morphogenetic protein type II receptor (BMPRII), these mutations have not been observed in SSc-PAH. My initial aim was to investigate BMPRII expression and downstream signalling pathways in whole lung tissue and explant cultured fibroblasts derived from a murine model of SSc (TβRIIΔk-fib) that is susceptible to developing PAH Complementary studies examined SSc or control lung tissue and fibroblasts. My results suggest reduced BMPRII levels, impaired signalling and altered receptor turnover could be due to increased TGFβ activity in a model of SSc-PAH. Similarly a significant reduction in BMPRII expression is observed in SSc lung tissue and fibroblasts. Increased proteasomal degradation of BMPRII appears to underlie this and may result from heightened TGFβ activity. Proteasomal inhibition restored BMPRII expression and cellular responses. Collectively suggesting that impaired TGFβ/BMP signalling leading to increased receptor degradation, may promote PAH susceptibility in SSc and provide a unifying mechanism across different forms of PAH. Since more than one cell type contribute to the development of PAH and the pathophysiology of the disease BMPRII expression and TGFβ responses in pulmonary arterial smooth muscle cells (PASMCs) were also investigated. Initial studies generated a synthetic “disease” like PASMC that also displayed a reduction in BMPRII expression and increased response to TGFβ, which was similar to IPAH cells Finally, the role of epigenetic inhibition in the TβRIIΔk-fib model was investigated. The epigenetic inhibitor JQ1 was able to attenuate the spontaneous development of PAH in the TβRIIΔk-fib model of PAH. Taken together, work described in this thesis strongly suggests that a reduction in BMPRII is a susceptibility factor to the development of PAH in a pre-clinical model of SSc and in SSc patients