No abstract

No abstract

This paper <span lang="EN-US">implements a data aware early prediction of hypertension-based diseases. Automated data preprocessing method that adopts for both balanced and unbalanced data is the data aware method included in the disease classification algorithm. Proposed data aware data preprocessing method is evaluated on the ensemble learning based classification algorithm for early disease prediction. Data aware preprocessing method adopts isolation forest algorithm for outlier detection as part of the automation. Automated sampling method of applying the sampling corresponding to either balanced or unbalanced data is adopted. Performance evaluation of the proposed data aware algorithm using isolation forest algorithm for anomaly detection is experimented. Python based implementation of the proposed data aware classification algorithm inferred a better area under the curve (AUC) receiver operating characteristics (ROC) curve for isolation forest implementation in data preprocessing automation thus developed. While the individual classifiers multilayer perceptron classifier approached till 0.918 (AUC) in the ROC-AUC curve. The ensemble learning algorithm that included multilayer perceptron classifier, logistic regression classifier, support vector classifier and decision tree algorithm with the isolation forest-based anomaly detection algorithm performed better than the individual machine learning algorithm with 0.922 (AUC) in the ROC-AUC curve.</span>

No abstract

In contemporary medicine, cardiovascular disease is a major public health concern. Cardiovascular diseases are one of the leading causes of death worldwide. They are classified as vascular, ischemic, or hypertensive. Clinical information contained in patients' Electronic Health Records (EHR) enables clinicians to identify and monitor heart illness. Heart failure rates have risen dramatically in recent years as a result of changes in modern lifestyles. Heart diseases are becoming more prevalent in today's medical setting. Each year, a substantial number of people die as a result of cardiac pain. The primary cause of these deaths is the improper use of pharmaceuticals without the supervision of a physician and the late detection of diseases. To improve the efficiency of the classification algorithms, we construct a data pre-processing stage using feature selection. Experiments using unidirectional and bidirectional neural network models found that a Deep Learning Modified Neural Network (DLMNN) model combined with the Pet Dog-Smell Sensing (PD-SS) algorithm predicted the highest classification performance on the UCI Machine Learning Heart Disease dataset. The DLMNNbased PDSS achieved an accuracy of 94.21%, an F-score of 92.38%, a recall of 94.62%, and a precision of 93.86%. These results are competitive and promising for a heart disease dataset. We demonstrated that a DLMNN framework based on deep models may be used to solve the categorization problem for an unbalanced heart disease dataset. Our proposed approach can result in exceptionally accurate models that can be utilized to analyze and diagnose clinical real-world data.