Abstract
The early diagnosis of chronic diseases plays a vital role in the field of healthcare communities and biomedical, where it is necessary for detecting the disease at an initial phase to reduce the death rate. This paper investigates the use of feature selection, dimensionality reduction and classification techniques to predict and diagnose chronic disease. The appropriate selection of attributes plays a crucial role in improving the classification accuracy of the diagnosis systems. Additionally, dimensionality reduction techniques effectively improve the overall performance of the machine learning algorithms. On chronic disease databases, the classification techniques deliver efficient predictive results by developing intelligent, adaptive and automated system. Parallel and adaptive classification techniques are also analyzed in chronic disease diagnosis which is used to stimulate the classification procedure and to improve the computational cost and time. This survey article represents the overview of feature selection, dimensionality reduction and classification techniques and their inherent benefits and drawbacks.
Highlights
In recent decades, chronic disease is the biggest threats to human life, which is essential to diagnose and predict chronic disease prior to reducing the mortality rate
FEATURE SELECTION AND DIMENSIONALITY REDUCTION TECHNIQUES IN CHRONIC DISEASE DIAGNOSIS In data mining, feature selection and dimensionality reduction are the most commonly used pre-processing techniques that minimize the data by reducing the irrelevant attributes in the databases
Accuracy: It is a most intuitive performance measure used in chronic disease diagnosis, where it is the ratio of correctly predicted observation among the total observations
Summary
Chronic disease is the biggest threats to human life, which is essential to diagnose and predict chronic disease prior to reducing the mortality rate. The relevant feature diagnosis helps in removing the redundant attributes and irrelevant information from the chronic disease databases, which. FEATURE SELECTION AND DIMENSIONALITY REDUCTION TECHNIQUES IN CHRONIC DISEASE DIAGNOSIS In data mining, feature selection and dimensionality reduction are the most commonly used pre-processing techniques that minimize the data by reducing the irrelevant attributes in the databases. It facilitates better data visualization, improves data understandability, and minimizes the training time of classification techniques in chronic disease diagnosis.
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