Random State Initialized Logistic Regression for Improved Heart Attack Prediction

One of the primary causes of death in Indonesia is heart attacks. Therefore, an effective method of pre-diction is required to determine whether a patient is experiencing a heart attack. One efficient approach is to use machine learning models. However, it is still rare to find machine learning models that have good performance in predicting heart attacks. This study aims to develop a machine learning model on Logistic Regression algorithm in predicting heart attack. Logistic Regression is one of the machine learning meth-ods that can be used to study the relationship between a binary response variable [0,1] and a set of pre-dictor variables, and can be used directly to calculate probabilities. In this study, a random state is ini-tialized in the Logistic Regression model in order to stabilize the training of the machine learning model and increase the precision of the proposed method. The results of this study show that the proposed model can be a method that has good performance in predicting heart attack disease

Deep Featured Adaptive Dense Net Convolutional Neural Network Based Cardiac Risk Prediction in Big Data Healthcare Environment

In recent days, cardiac vascular disease has been one of the deadliest health-affecting factors causing sudden death. So, the importance of early risk prediction through feature analysis has become a big problem in data analysis because more nonlinear time series data increase the feature dimension. Irrelevant feature dimension scaling affects the prediction accuracy and leads to classification inaccuracy. To resolve this problem, we propose an Enhanced Healthcare data analysis model for cardiac data prediction using an adaptive Deep Featured Adaptive Convolution Neural Network for early risk identification. Initially, the preprocessing was augmented to formalize the time series data collected from the CVD-DS dataset. Then the feature evaluation was carried out with the Relative Subset Clustering (RSC) approach. The Cardiac Deficiency Prediction rate (CDPr) was estimated to identify the relational feature to subset margins. Based on the CDPr weight the feature is extracted using Cross-Over Mutual Scaling Feature Selection Model (CMSFS). The selected features get with a deep neural classifier based on logical neurons. They are then constructed into a Dense Net Convolution Neural Network (DN-CNN) classifier to feed forward the feature values and predict the Disease Affection Rate (DAR) by class category. The proposed system produces high prediction accuracy in classification, precision, and recall rate to support premature treatment for early cardiac disease risk prediction.

ROBUST DETECTION OF CORONARY HEART DISEASE USING MACHINE LEARNING ALGORITHMS

Predicting whether or not someone will get heart or cardiac disease is now one of the most difficult jobs in the area of medicine. Heart disease is responsible for the deaths of about one person per minute in the contemporary age. Processing the vast amounts of data that are generated in the field of healthcare is an important application for data science. Because predicting cardiac disease is a difficult undertaking, there is a pressing need to automate the prediction process to minimize the dangers that are connected with it and provide the patient with timely warning. The chapter one in this thesis report highlights the importance of this problem and identifies the need to augment the current technological efforts to produce relatively more accurate system in facilitating the timely decision about the problem. The chapter one also presents the current literature about the theories and systems developed and assessed in this direction.This thesis work makes use of the dataset on cardiac illness that can be found in the machine learning repository at UCI. Using a variety of data mining strategies, such as Naive Bayes, Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), and Random Forest, the work that has been reported in this thesis estimates the likelihood that a patient would develop heart disease and can categorize the patient\u27s degree of risk. The performance of chosen classifiers is tested on chosen feature space with help of feature selection algorithm. On Cleveland heart datasets of heart disease, the models were placed for training and testing. To assess the usefulness and strength of each model, several performance metrics are utilized, including sensitivity, accuracy, AUC, specificity, ROC curve and F1-score. The effort behind this research leads to conduct a comparative analysis by computing the performance of several machine learning algorithms. The results of the experiment demonstrate that the Random Forest and Support Vector machine algorithms achieved the best level of accuracy (94.50% and 91.73% respectively) on selected feature space when compared to the other machine learning methods that were employed. Thus, these two classifiers turned out to be promising classifiers for heart disease prediction. The computational complexity of each classifier was also investigated. Based on the computational complexity and comparative experimental results, a robust heart disease prediction is proposed for an embedded platform, where benefits of multiple classifiers are accumulated. The system proposes that heart disease detection is possible with higher confidence if and only if many of these classifiers detect it. In the end, results of experimental work are concluded and possible future strategies in enhancing this effort are discussed