Multi Disease Prediction System using Random Forest Algorithm in Healthcare System

Shanthakumari, R.; Nalini, C.; Vinothkumar, S.; Roopadevi, E.M.; Govindaraj, B.

doi:10.1109/mecon53876.2022.9752432

Cited by 11 publications

(3 citation statements)

References 2 publications

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“…Again, random forest achieved the best macro F1score of 94%. Thus, our result is consistent with the study [26,[28][29][30][31] that showed RF as an off-the-shelf model specifically in the medical domain, where feature importance is significant in terms of interoperability (to patients). Thus, the random forest algorithm was deemed the best predictor for the incidence of stroke.…”

Section: Model Classification Resultssupporting

confidence: 92%

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Prediction of Stroke Disease with Demographic and Behavioural Data Using Random Forest Algorithm

Shobayo,

Zachariah,

Odusami

et al. 2023

Analytics

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Stroke is a major cause of death worldwide, resulting from a blockage in the flow of blood to different parts of the brain. Many studies have proposed a stroke disease prediction model using medical features applied to deep learning (DL) algorithms to reduce its occurrence. However, these studies pay less attention to the predictors (both demographic and behavioural). Our study considers interpretability, robustness, and generalisation as key themes for deploying algorithms in the medical domain. Based on this background, we propose the use of random forest for stroke incidence prediction. Results from our experiment showed that random forest (RF) outperformed decision tree (DT) and logistic regression (LR) with a macro F1 score of 94%. Our findings indicated age and body mass index (BMI) as the most significant predictors of stroke disease incidence.

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Section: Model Classification Resultssupporting

confidence: 92%

“…Each tree classifier is created by independently sampling a random vector from the input vector. The classification of an input vector is determined by the collective vote of each tree, selecting the class that receives the highest number of votes [27][28][29][30].…”

Section: Random Forestmentioning

confidence: 99%

Prediction of Stroke Disease with Demographic and Behavioural Data Using Random Forest Algorithm

Shobayo,

Zachariah,

Odusami

et al. 2023

Analytics

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show abstract

“…ANOVA is one of the feature selection techniques used by the author to find the best features for improved accuracy. The study [19] suggested a model predict numerous diseases as there are very few suggestions made about the detection of numerous diseases. The author takes into consideration conditions such as heart disease, diabetes, and kidney disease.…”

Section: A Filter Methodsmentioning

confidence: 99%

Hybrid Feature Selection Algorithm and Ensemble Stacking for Heart Disease Prediction

Zaini¹,

Awang²

2023

IJACSA

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In cardiology, as in other medical specialties, early and accurate diagnosis of heart disease is crucial as it has been the leading cause of death over the past few decades. Early prediction of heart disease is now more crucial than ever. However, the state-of-the-art heart disease prediction strategy put more emphasis on classifier selection in enhancing the accuracy and performance of heart disease prediction, and seldom considers feature reduction techniques. Furthermore, there are several factors that lead to heart disease, and it is critical to identify the most significant characteristics in order to achieve the best prediction accuracy and increase prediction performance. Feature reduction reduces the dimensionality of the information, which may allow learning algorithms to work quicker and more efficiently, producing predictive models with the best rate of accuracy. In this study, we explored and suggested a hybrid of two distinct feature reduction techniques, chi-squared and analysis of variance (ANOVA). In addition, using the ensemble stacking method, classification is performed on selected features to classify the data. Using the optimal features based on hybrid features combination, the performance of a stacking ensemble based on logistic regression yields the best result with 93.44%. This can be summarized as the feature selection method can take into account as an effective method for the prediction of heart disease.

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Deep Convolutional Neural Network Model for Classifying Alzheimer’s Disease

Shanthakumari¹,

Devi²,

Vinothkumar³

et al. 2023

Lecture Notes in Networks and Systems

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Multi Disease Prediction System using Random Forest Algorithm in Healthcare System

Cited by 11 publications

References 2 publications

Prediction of Stroke Disease with Demographic and Behavioural Data Using Random Forest Algorithm

Prediction of Stroke Disease with Demographic and Behavioural Data Using Random Forest Algorithm

Hybrid Feature Selection Algorithm and Ensemble Stacking for Heart Disease Prediction

Deep Convolutional Neural Network Model for Classifying Alzheimer’s Disease

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