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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Journal of Robotics and Control (JRC) Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Assegie, Tsehay Admassu
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Health Professions Materials Science & Nanotechnology Mechanical Engineering

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Journal : Journal of Robotics and Control (JRC)

 1 
An optimized K-Nearest Neighbor based breast cancer detection Assegie, Tsehay Admassu
Journal of Robotics and Control (JRC) Vol 2, No 3 (2021): May (Forthcoming Issue)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In this research, a grid search is employed to find the optimal parameter and an optimized K-Nearest Neighbor (KNN) based breast cancer detection model is proposed. The grid search is used to find the best combinations of parameters that could produce better breast cancer detection accuracy. Moreover, this study explored the effect of parameter tuning on the performance of KNN algorithm foe breast cancer detection. The findings of this research reveals that parameter tuning has a significant effect on the performance of the proposed model. The effect of parameter tuning on the performance of KNN algorithm is experimentally tested using Wisconsin breast cancer dataset collected from kaggle data repository. Finally, we have compared the performance of the KNN algorithm with the tuned hyper-parameter and with default hyper-parameter. The result analysis on the performance of the KNN algorithm on breast cancer detection on the test dataset reveals that the accuracy of the proposed optimized model is 94.35% and the performance of the KNN algorithm with the default hyper-parameter is 90.10%.
Early Prediction of Gestational Diabetes with Parameter-Tuned K-Nearest Neighbor Classifier Assegie, Tsehay Admassu; Suresh, Tamilarasi; Purushothaman, Raguraman; Ganesan, Sangeetha; Kumar, Napa Komal
Journal of Robotics and Control (JRC) Vol 4, No 4 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i4.18412

Abstract

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.
Co-Authors Adimas, Ayenew Kassie Alagarsamy, Manjunathan Ayalew, Amogne Andualem Braide, Sepiribo Lucky Chowdhury, Ismatul Jannat Chowdhury, Nur Mohammad Eneh, Joy Nnenna Ganesan, Sangeetha Getachew Chikol, Yilachew Gupta, Shashi Kant Kalyan Kumar, Angati Kumar Napa, Komal Kumar, Napa Komal Manivannan, Bommy Markus, Elisha Didam Mazumdar, Indu Mekonen, Mareye Zeleke Mohmmad, Shaimaa Hadi Ozue, ThankGod Izuchukwu Palit, Shamik Prasad, Battula Krishna Priya Kompala, Chandi Purushothaman, Raguraman Quansah, Andrew Radhakrishnan, Chirakkal Rathish Rajangam, Balamurugan Salau, Ayodeji Olalekan Singh, Hemant Kumar Sinha Roy, Chandrima Suresh, Tamilarasi Tegegne, Melaku Lake Tin, Ting Tin Yanhui, Liu Zeleke Mekonen, Mareye