Prediction of hypertention drug therapy response using K-NN imputation and SVM algorithm

Muflikhah, Lailil; Hidayat, Nurul; Hariyanto, Dimas Joko

doi:10.11591/ijeecs.v15.i1.pp460-467

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…The most correlated features are combined into one group and the less correlated features are put in another group as mentioned in Figure 1. In the proposed method, iterative imputer is applied to features of the first group whereas the second group of features uses KNN imputer [28]. Table 1 represents these features for different stations.…”

Section: Data Imputationmentioning

confidence: 99%

A framework for cloud cover prediction using machine learning with data imputation

Mandal,

Sarode

2024

IJECE

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The climatic conditions of a region are affected by multiple factors. These factors are dew point temperature, humidity, wind speed, and wind direction. These factors are closely related to each other. In this paper, the correlation between these factors is studied and an approach has been proposed for data imputation. The idea is to utilize all these features to obtain the prediction of the total cloud cover of a region instead of removing the missing values. Total cloud cover prediction is significant because it affects the agriculture, aviation, and energy sectors. Based on the imputed data which is obtained as the output of the proposed method, a machine learning-based model is proposed. The foundation of this proposed model is the bi-directional approach of the long short-term memory (LSTM) model. It is trained for 8 stations for two different approaches. In the first approach, 80% of the entire data is considered for training and 20% of the data is considered for testing. In the second approach, 90% of the entire data is accounted for training and 10% of the data is accounted for testing. It is observed that in the first approach, the model gives less error for prediction.

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Section: Data Imputationmentioning

confidence: 99%

A framework for cloud cover prediction using machine learning with data imputation

Mandal,

Sarode

2024

IJECE

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“…Some popular imputation methods are mean, median, mode, clustering, k-NN imputation, and class mean imputation [25]. In K-NN imputation, filling in missing values is done by taking into account the vector distance between attributes [26]. In class mean imputation, the missing value will be replaced by the mean value of all available values in a related group or class [12].…”

Section: Missing Data Imputationmentioning

confidence: 99%

Application of Data Mining and Imputation Algorithms for Missing Value Handling: A Study Case Car Evaluation Dataset

Widyananda

Purnomo

Aswin

et al. 2023

Iraqi Journal of Science

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Data mining is a data analysis process using software to find certain patterns or rules in a large amount of data, which is expected to provide knowledge to support decisions. However, missing value in data mining often leads to a loss of information. The purpose of this study is to improve the performance of data classification with missing values, precisely and accurately. The test method is carried out using the Car Evaluation dataset from the UCI Machine Learning Repository. RStudio and RapidMiner tools were used for testing the algorithm. This study will result in a data analysis of the tested parameters to measure the performance of the algorithm. Using test variations: performance at C5.0, C4.5, and k-NN at 0% missing rate, performance at C5.0, C4.5, and k-NN at 5–50% missing rate, performance at C5.0 + k-NNI, C4.5 + k-NNI, and k-NN + k-NNI classifier at 5–50% missing rate, and performance at C5.0 + CMI, C4.5 + CMI, and k-NN + CMI classifier at 5–50% missing rate, The results show that C5.0 with k-NNI produces better classification accuracy than other tested imputation and classification algorithms. For example, with 35% of the dataset missing, this method obtains 93.40% validation accuracy and 92% test accuracy. C5.0 with k-NNI also offers fast processing times compared with other methods.

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“…Several other studies using SVM and its kernel are Pratama [15] which uses the default SVM for classification of complaint text with an accuracy of 89%, Nurajijah [16] which uses SVM, naïve bayes and decision trees for calcification of financing approvals where SVM excels with an accuracy of 90%. , naïve Bayes 77% and decision tree 89%, Pratyo [17] uses SVM with a polynomial kernel for classification of public opinion regarding Covid-19 which provides 82% accuracy, Wirasati [18] uses 3 SVM kernels for thalassemia classification which obtains almost the same accuracy for the three kernels, namely 99.63% for RBF, 98.23% for linear, and 97.9% for polynomial.…”

Section: Introductionmentioning

confidence: 99%

Classification of Public Opinion on Online Learning Policies using Various Support Vector Machine’s Kernel

Husni,

Arif Muntasa,

Mochamad Dani Hartanto

2023

Technium

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The COVID-19 pandemic has resulted in significant changes in the education sector. The government issued a policy so that learning must be carried out online from home. This policy became a polemic for teachers and students so that pro and con opinions emerged on social media, especially Twitter. Sentiment analysis of public opinion is an interesting study. Standard classification algorithms such as k-Nearest neighbours, naïve bayes, decision tree, random forest, and support vector machine (SVM) can categorize these opinions in a short time with good accuracy. Many studies show that SVM is more accurate than all other classification methods. SVM works using kernels, including Linear, Polynomial and Radial Basis Functions (RBF) where each kernel requires different parameters. The linear kernel only requires one parameter, namely c (Cost). The RBF kernel requires 2 parameters, c and ɣ (gamma) while the Polynomial kernel uses 2 parameters, c and degrees. SVM does not have default values for these parameters and are based on experience and experimentation. The wider the range of parameters, the more likely the classifier obtains the optimal values. This study tries some parameters values of SVM kernels for text classification based on sentiment. Testing using 5-fold cross validation and confusion matrix show that SVM with a linear kernel provides the best performance with an accuracy of above 84%.

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Prediction of hypertention drug therapy response using K-NN imputation and SVM algorithm

Cited by 4 publications

References 8 publications

A framework for cloud cover prediction using machine learning with data imputation

A framework for cloud cover prediction using machine learning with data imputation

Application of Data Mining and Imputation Algorithms for Missing Value Handling: A Study Case Car Evaluation Dataset

Classification of Public Opinion on Online Learning Policies using Various Support Vector Machine’s Kernel

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