scholarly journals Chronic Disease Prediction Using Machine Learning

IJARCCE ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 287-292
Author(s):  
Meghana M ◽  
Shashank S ◽  
Tojo Mathew

Our work aims for economical disease diagnostics, by asking the user for Prognosis and symptoms, accurate disease prediction has been strived for. In aspiration for social welfare, the cost of using the product built is almost free, the prediction can be done using any one of the six algorithms, five out of which are total free of cost for use, those five being KNN, Naïve Bayes, SVM , Logistic Regression, K Means Classifier. The one, that gives out predictions with most accuracy, i.e., Decision Trees Classifier, has been made paid, others are not to be paid for, for using.How this product would be functioning is simple: User logs in , openCV has been used for it, that brings the user to the section where user is briefed about models working on different algorithms, each algorithm having different accuracy, thus further, which model he/ she should choose. On choosing model of their choice, they fill their symptoms and prognosis, that yields them their final result of name of their disease.Services like these are greatly needed , looking at large many number of people in our society, who are unfortunately not able to afford them, when priced heavily, or even moderately. Such products can help save many a lives, notify sufferer about his chronic disease at early stage, inform about deficiency diseases, that are very controllable, if get known about, early.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Haohui Lu ◽  
Shahadat Uddin

AbstractChronic disease prediction is a critical task in healthcare. Existing studies fulfil this requirement by employing machine learning techniques based on patient features, but they suffer from high dimensional data problems and a high level of bias. We propose a framework for predicting chronic disease based on Graph Neural Networks (GNNs) to address these issues. We begin by projecting a patient-disease bipartite graph to create a weighted patient network (WPN) that extracts the latent relationship among patients. We then use GNN-based techniques to build prediction models. These models use features extracted from WPN to create robust patient representations for chronic disease prediction. We compare the output of GNN-based models to machine learning methods by using cardiovascular disease and chronic pulmonary disease. The results show that our framework enhances the accuracy of chronic disease prediction. The model with attention mechanisms achieves an accuracy of 93.49% for cardiovascular disease prediction and 89.15% for chronic pulmonary disease prediction. Furthermore, the visualisation of the last hidden layers of GNN-based models shows the pattern for the two cohorts, demonstrating the discriminative strength of the framework. The proposed framework can help stakeholders improve health management systems for patients at risk of developing chronic diseases and conditions.


Author(s):  
Anantvir Singh Romana

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.


Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.


2021 ◽  
Vol 1916 (1) ◽  
pp. 012092
Author(s):  
N Karthikeyan ◽  
P Padmanaban ◽  
A Prasanth ◽  
D Ragunath

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