Heart Disease Prediction based on Ensemble Classification Model with Tuned Training Weights
Heart Disease Prediction based on Ensemble Classification Model with Tuned Training Weights
|© 2022 by IJETT Journal|
|Year of Publication : 2022|
|Authors : Parvathaneni Rajendra Kumar, Suban Ravichandran, S. Narayana
|DOI : 10.14445/22315381/IJETT-V70I4P206|
How to Cite?
Parvathaneni Rajendra Kumar, Suban Ravichandran, S. Narayana, "Heart Disease Prediction based on Ensemble Classification Model with Tuned Training Weights," International Journal of Engineering Trends and Technology, vol. 70, no. 4, pp. 59-81, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I4P206
Heart disease (HD) is the most serious human disease, causing havoc on people`s health. Heart disease detection must be accurate and timely to prevent and cure heart failure. In many instances, the diagnosis of HD based on standard medical history is seen as unreliable. Therefore, this paper introduces a novel HD prediction system that includes five major phases such as (a) Preprocessing, (b) Imbalance processing, (c)Feature extraction, (d) Feature Selection and (e) Classification. Originally, the input data is given to the preprocessing phase. Subsequently, the imbalance processing phase is carried out, where an improved strategy for the class imbalance process is performed. The features, including raw features, improved mutual information, higher-order statistical features, entropy, correlation, and statistical features, are extracted in the feature extraction phase. Moreover, appropriate features will be selected from the extracted features in the feature selection phase, for which an improved ReliefF process will be carried out. These selected features is then subjected to the classification phase, where the ensemble classifiers include Neural Network (NN), Recurrent Neural Network (RNN), Random Forest (RF), and K-Nearest Neighbour (k-NN) model. Here, the output of NN, RNN, and RF is given as the input of k-NN. To make the system more precise in disease prediction, the weights of NN and RNN are optimally tuned by a Self-improved Shark Smell Optimization with Gaussmap Estimation and Cycle crossover Operation (SISSGECO) model. Then, the final output is obtained effectively in a precise manner. Finally, the outcomes of the adopted scheme are computed to the other extant schemes in terms of various measures like precision, sensitivity, accuracy, specificity, NPV, MCC, FPR, F1-score, and FNR, respectively.
Heart Disease Prediction, Imbalance Processing, Improved ReliefF, Ensemble Classifiers, Optimization.
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