Hybrid Machine Learning Model for Chronic Disease Prediction - Salman , Rahama; Gupta, Subodhini (International Journal of Intelligent Systems and Applications in Engineering, 2023)
The previously suggested chronic disease prediction techniques are incapable of acquiring efficiency in feature extraction, outlier removal and classification. This research work is conducted to tackle the limitations of these methods. After eliminating the existing drawbacks, the accuracy to predict the chronic disease is augmented consequently. Therefore, the fundamental emphasize is on predicting the disease on the basis of economic and social data, and analyzing the trends of chronic diseases depending upon the epidemiological data. This work suggests a hybrid framework in which Random Forest (RF) is integrated with Logistic Regression (LR). The initial algorithm is implemented for extracting the features, and the latter one is exploited for classifying the diseases. Logistic Regression algorithm makes the deployment of extracted features as input to classify the data. Python is executed to simulate the suggested framework. Various metrics, namely accuracy, precision and recall are utilized to analyze the results.