Chronic Kidney Disease Diagnosis using Decision Tree Algorithms

Ilyas, Hamida; Ali, Sajid; Ponum, Mahvish; Hasan, Osman; Mahmood, Muhammad Tahir

doi:10.21203/rs.3.rs-34685/v1

Cited by 7 publications

(8 citation statements)

References 12 publications

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“…Of all used machine learning algorithms, the most accuracy among all ML algorithms was related to the SVMs algorithm with 0.97. In our study the accuracy of SVMs algorithm among all of the applied ML algorithms was 0.82 which was lower than the study by Ilyas et al rate (15).…”

Section: Discussioncontrasting

confidence: 86%

Using machine learning method for classification body mass index of people for clinical decision

et al. 2022

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Introduction: Body mass index (BMI) is an acceptable method to measure overweight and obesity among the population. Objectives: The aim of this study was evaluating the application of machine learning algorithms for classifying body mass index for clinical purposes. Patients and Methods: In this descriptive study, we selected the dataset of 1316 people who selected randomly from all area of Ardabil city in Iran. Dataset included demographic and anthropometric data. Classification algorithms such as random forest (RF), Gaussian Naive Bayes (GNB), decision tree (DT), support vector machines (SVM), multi-layer perceptron (MLP), K-nearest neighbors (KNN) and logistic regression (LR) with 10-fold cross-validation were conducted to classify the data based on BMI. The performance of algorithms was evaluated with precision, recall, mean squared errors (MSE) and accuracy indices. All programing done by Python 3.7 in Jupyter Notebook. Results: According to the BMI, 603(45.8%) of all samples were normal and 713 (54.2%) were at-risk. The precision of RF, GNB, DT, SVM, MLP, KNN and LR for people at risk were 0.93, 0.86, 0.99, 0.82, 100, 0.82 and 0.99 respectively. Additionally, the accuracy of RF, GNB, DT, SVM, MLP, KNN and LR were 95%, 83%, 100%, 82%, 100%, 82% and 100 %. Conclusion: The comparison of the classifying algorithms showed that, the LR, MLP and DT had the higher accuracy than the other algorithms in detecting of people at-risk.

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Section: Discussioncontrasting

confidence: 86%

Using machine learning method for classification body mass index of people for clinical decision

et al. 2022

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“…In [5], The author analyzed how we can predict CKD (Chronic Kidney Disease) in its earliest stage and concluded that it can be done with J48 with an accuracy of 85. 5%.…”

Section: Chronic Kidney Diseasementioning

confidence: 99%

Tongue Diagnosis using CNN for Disease Detection

Prathibha¹,

Saradha²,

Jothika³

et al. 2022

IJEER

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In this modern lifestyle, technologies are helping us to maintain our finances, our household things, shopping, and so on. In our research work, we have proposed an application that would tell you the disease or infection that you may have with the help of the developing technology. In this pandemic period, we have to be safer and more Responsible. We have to avoid visiting public places as much as possible for us and our society. Our main aim is to reduce death rates which are all caused due to finding the disease at its final stage because of hesitation to visit the hospital during this pandemic or because of our carelessness. We can overcome it by checking for diseases or infections frequently using a mobile app. In this research work, we are planning to develop a mobile application using which we can frequently check for diseases or infections since we always have our mobile phones with us. With this application, we can detect the percentage of chance of disease that the user may have through tongue diagnosis by considering changes in various tongue factors. The basic objective of the research work is to make people know about their body condition at an earlier stage more easily and quickly with their smart mobile. In this report, we have included the literature survey made for this proposed system, existing works, software requirements, the proposed system, etc.

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“…The authors of [2] applied artificial neural networks for various algorithms. The KNN algorithm (K Nearest Neighbor) is also being employed, and the RFT algorithm is implemented.…”

Section: ░ 2 Related Workmentioning

confidence: 99%

“…It detects the label category relevant to the training document in a similar test document. This method is used in KNN [2] to classify things into object-based classes. Only the function is estimated locally, and all computations differ until classification.…”

Section: Decision Treesmentioning

confidence: 99%

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An Improved Deep Learning Approach for Prediction of The Chronic Kidney Disease

Akanksha

Suganeshwari

2022

IJEER

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Kidney function is harmed by chronic kidney disease, leading to renal failure. Machine learning and data mining come in handy to detect kidney disease. Machine learning employs a variety of algorithms to make predictions and classify data. CT scans have been used to detect chronic renal disease. When CT scans are used to diagnose disease in the kidney, cross-infection occurs, and the results are delayed. The authors of the prior study developed a model for categorizing chronic renal illness utilizing multiple classification methods. A unique deep learning model is presented in this study for the early identification and prognosis of Chronic Kidney Disease (CKD). This study aims to build a neural network and evaluate its performance compared to other cutting-edge machine learning methods. Compared to the four different classifiers (K-Nearest Neighbor (KNN), Random Forest, Naive Bayes classifier, and probabilistic neural network), the suggested Deep neural model fared better by reaching higher accuracy. Nephrologists may find the proposed method helpful in the early detection of CKD.

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Chronic Kidney Disease Diagnosis using Decision Tree Algorithms

Cited by 7 publications

References 12 publications

Using machine learning method for classification body mass index of people for clinical decision

Using machine learning method for classification body mass index of people for clinical decision

Tongue Diagnosis using CNN for Disease Detection

An Improved Deep Learning Approach for Prediction of The Chronic Kidney Disease

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