Collection of demographic data of risk factors for IHD. To develop a simple approach to predict risk of developing Ischemic Heart Disease (IHD) (вЂњHeart AttackвЂќ) by using most widely used technology of smartphone. Paper Title: Smartphone Based вЂњHeart AttackвЂ™вЂ™ Risk Prediction Initiation of A Clinically Simple Approach Our thesis product may reduce this limitation and promote a risk evaluation on time. Currently available tools has mandatory input of lipid values which makes them underutilized by population though those risk calculators bear excellent academic importance. Our thesis is motivated to make simple approach to detect the heart disease risk and aware the population to get themselves evaluated by a cardiologist On comparing and categorizing the patients whose data was obtained for generating the score we found there was significant correlation of having a cardiac event when Low and High category was compared and p value = 0.0004. The risk was classified into Low, Medium and High for IHD. The data was mined with data mining technology in computer science and a score was generated. The clinical data from 787 patients was correlated and analyzed with the risk factors like Hypertension, Diabetes, Dyslipidemia, Smoking, Family History, Exercise, Stress and existing clinical symptom which may suggest underlying non detected IHD. Thus we want to develop a smart phone based system that can initially predict heart disease risk. In our country most of our people cannot afford this expensive diagnosis cost. Medical diagnosis is very important but very expensive. The healthcare industry collects huge amounts of data, however that is not mined. Heart disease is now very frequent in Bangladesh. Khulna University Thesis Title: Heart Diseases Prediction Using Clinical Data And Data Mining Approaches This is why the proposed method is not suitably applicable to Cleveland dataset. The Cleveland hospital data contains 303 patientsвЂ™ data with only 13 features whereas the collected dataset contains 506 patientвЂ™s data with 28 nicely correlated IHD risk factors. The observed IHD prediction accuracy was 80.46-83.77% without applying the proposed processing techniques, but the accuracy degraded to 79.80-81.46% applying the proposed processing techniques. The same processing technique and classification algorithms were applied to the Cleveland hospital dataset to validate our prediction approach. Random Forest showed the best prediction performance with an accuracy of 97.63%. IHD prediction accuracy was observed ranges from 95.85% to 97.63% with different classifier algorithm. Artificial Neural Network (ANN), Bagging, Logistic Regression, and Random Forest to predict IHD using the most significant 28 risk factors. 10-fold cross-validation technique was applied with different classification algorithms e.g. Among 151 collected features only 28 features showed high correlations with IHD based on 0.05 significance level and information gain 1% or above.

The significant risk factors of IHD were determined by using Chi-Square correlation, Ranking the features based on information gain and Best First Search techniques. Then IHD was predicted using significant risk factors by applying different classifier algorithms. the features which are significantly correlated with IHD by applying different feature selection techniques. First, it was tried to identify the significant risk factors of IHD i.e. Total 506 patientвЂ™s data with a maximum of 151 features including historic, symptomatic and pathologic findings were collected from AFC Fortis Escort Heart Institute, Khulna, Bangladesh.

In this thesis work, it was tried to predict IHD on the basis of patient history, symptoms and pathological findings of patients with heart disease using computational intelligence. For this reason, early detection is very important for the proper management of patients prone to IHD. plaque deposition on the inner wall of arteries is a silent process, has no critical symptoms to get a warning before IHD. Ischemic heart disease (IHD) is a terrible experience that occurs when the flow of blood severely reduced or cut off due to plaque deposited on the inner wall of arteries that brings oxygen to the heart muscle, leads to the ischemic heart attack (IHA). Khulna University of Engineering and Technology (KUET) Thesis Title: Prediction on Ischemic Heart Disease using Machine Learning Approaches

Raihan chowdhury