Liver Diseases Prediction using KNN with Hyper Parameter Tuning Techniques

Ambesange, Sateesh; Nadagoudar, Ranjana; Uppin, Rashmi; Patil, Vilaskumar; Patil, Shruti; Patil, Sushma

doi:10.1109/b-htc50970.2020.9297949

Cited by 14 publications

(4 citation statements)

References 4 publications

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“…Grid search and hyperparameter tuning have been widely employed for improving the performance of the KNN algorithm. For instance, research article [30], [31], [32]. The study employed feature scaling due to its significance in improving the performance of the KNN model for diabetes diagnosis [33].…”

Section: Methodsmentioning

confidence: 99%

Early Prediction of Gestational Diabetes with Parameter-Tuned K-Nearest Neighbor Classifier

Assegie,

Suresh,

Purushothaman

et al. 2023

Journal of Robotics and Control

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Diabetes is one of the quickly spreading chronic diseases causing health complications, such as diabetes retinopathy, kidney failure, and cardiovascular disease. Recently, machine-learning techniques have been widely applied to develop a model for the early prediction of diabetes. Due to its simplicity and generalization capability, K-nearest neighbor (KNN) has been one of the widely employed machine learning techniques for diabetes prediction. Early diabetes prediction has a significant role in managing and preventing complications associated with diabetes, such as retinopathy, kidney failure, and cardiovascular disease. However, the prediction of diabetes in the early stage has remained challenging due to the accuracy and reliability of the KNN model. Thus, gird search hyperparameter optimization is employed to tune the K values of the KNN model to improve its effectiveness in predicting diabetes. The developed hyperparameter-tuned KNN model was tested on the diabetes dataset collected from the UCI machine learning data repository. The dataset contains 768 instances and 8 features. The study applied Min-max scaling to scale the data before fitting it to the KNN model. The result revealed KNN model performance improves when the hyperparameter is tuned. With hyperparameter tuning, the accuracy of KNN improves by 5.29% accuracy achieving 82.5% overall accuracy for predicting diabetes in the early stage. Therefore, the developed KNN model applied to clinical decision-making in predicting diabetes at an early stage. The early identification of diabetes could aid in early intervention, personalized treatment plans, or reducing healthcare costs reducing associated risks such as retinopathy, kidney disease, and cardiovascular disease.

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Section: Methodsmentioning

confidence: 99%

Early Prediction of Gestational Diabetes with Parameter-Tuned K-Nearest Neighbor Classifier

Assegie,

Suresh,

Purushothaman

et al. 2023

Journal of Robotics and Control

View full text Add to dashboard Cite

show abstract

“…Another drawback with [2] is that in memory utilization comparison and time comparison it exceeds when compared with CNN. S. Ambesange [3] uses the KNN model in a different manner giving a hyper parameter tuning twist to it. They have used feature selection considering the features which show a strong correlation.…”

Section: Related Workmentioning

confidence: 99%

Early-Stage Disease Prediction from Various Symptoms Using Machine Learning Models

Ajmera,

Pandey,

Singh

et al. 2024

EAI Endorsed Trans IoT

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Development and exploration of several Data analytics techniques in various real-time applications (e.g., Industry, Healthcare Neuroscience) in various domains have led to exploitation of it to extract paramount features from datasets. Following the introduction of new computer technology, the health sector had a significant transformation that compelled it to produce more medical data, which gave rise to a number of new disciplines of study. Quite a few initiatives are made to deal with the medical data and how its usage can be helpful to humans. This inspired academics and other institutions to use techniques like data analytics, its types, machine learning and different algorithms, to extract practical information and aid in decision-making. The healthcare data can be used to develop a health prediction system that can improve a person's health. Based on the dataset provided, making accurate predictions in early disease prediction benefits the human community.

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“…The performance of the KNN model is dependent on the value of the hyper-parameters; hence, this manuscript adopts the grid search method that aims to find the optimal parameters for the KNN model proposed by Ambesange [37]. The hyper-parameters of the KNN regressor defined by the result of the grid search are presented in Table 2.…”

Section: K-nearest Neighbor (Knn)mentioning

confidence: 99%

A Supply Chain-Oriented Model to Predict Crude Oil Import Prices in South Korea Based on the Hybrid Approach

Jo,

Kim,

Lee

et al. 2023

Sustainability

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Although numerous studies have explored key variables for forecasting crude oil prices, the role of supply chain factors has often been overlooked. In the face of global risks such as COVID-19, the Russia–Ukraine war, and the U.S.–China trade dispute, supply chain management (SCM) has evolved beyond an individual company’s concern. This research investigates the impact of a supply chain-oriented variable on the forecasting of crude oil import prices in South Korea. Our findings reveal that models incorporating the Global Supply Chain Pressure Index (GSCPI) outperform those without it, emphasizing the importance of monitoring supply chain-related variables for stabilizing domestic prices for policy makers. Additionally, we propose a novel hybrid factor-based approach that integrates time series and machine learning models to enhance the prediction performance of oil prices. This endeavor is poised to serve as a foundational step toward developing methodologically sound forecasting models for oil prices, offering valuable insights for policymakers.

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Liver Diseases Prediction using KNN with Hyper Parameter Tuning Techniques

Cited by 14 publications

References 4 publications

Early Prediction of Gestational Diabetes with Parameter-Tuned K-Nearest Neighbor Classifier

Early Prediction of Gestational Diabetes with Parameter-Tuned K-Nearest Neighbor Classifier

Early-Stage Disease Prediction from Various Symptoms Using Machine Learning Models

A Supply Chain-Oriented Model to Predict Crude Oil Import Prices in South Korea Based on the Hybrid Approach

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