Cancer Drug Classification using Artificial Neural Network with Feature Selection

Shobana, G.; Priya, N.

doi:10.1109/icicv50876.2021.9388542

Cited by 7 publications

(7 citation statements)

References 12 publications

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“…Figure 4. AUC of ANNFigures 5,6,7, and 8 also show that the resulting AUC curve is close to 0.5. Figures4, 5, 6, 7, and 8 present AUC results for five methods, using a data split of 70 to 30.…”

mentioning

confidence: 62%

“…Similarly, [5] classified drugs that cause QT syndrome by investigating ECG reports using SVM and KNN, resulting in 89% accuracy using ECG data from Physionet. [6] categorized cancer drugs using Logistic Regression, DT, ANN, RF, and Multi-Layer Perceptron, which specifically showed higher accuracy results. In addition, [7] found that K-NN outperformed Naive Bayes when comparing the two methods for drug molecule classification using biochemical data taken from PubChem.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of the Accuracy of Drug User Classification Models Using Machine Learning Methods

Basuni,

Amril Mutoi Siregar

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Cases of drug abuse are on the rise, with many users entering the addiction phase, often resulting in overdose and death. Drugs are chemical compounds that are capable of affecting biological functions, can induce feelings of happiness and reduce pain. To address this growing problem, a proactive measure is needed. Therefore, this study aims to classify drug users and non-users, so that health workers and therapists can educate about the dangers of drugs to non-users and rehabilitate drug users. This study uses drug consumption data taken from the UCI Irvine Machine Learning Repository. The data consists of 1885 rows with 32 attributes and 2 classes, where there are 18 types of legal and illegal drugs. This research utilizes machine learning methods, specifically Artificial Neural Network (ANN), Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF), in addition to evaluation methods such as Confusion Matrix and Area Under Curve (AUC). The results showed that RF outperformed the other methods, with accuracy, precision, and recall of 93%, and an f1-score of 89%, while the AUC value was still suboptimal at 0.66. DT had the worst results, with 82% accuracy, 87% precision, 82% recall, 84% f1-score, and an AUC value of 0.56. With these results, this research can be continued into an application that can classify drug users and non-users.

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mentioning

confidence: 62%

Section: Introductionmentioning

confidence: 99%

Comparison of the Accuracy of Drug User Classification Models Using Machine Learning Methods

Basuni,

Amril Mutoi Siregar

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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“…ANN is one method that can be used in the classification process by presenting a fairly good level of performance [37]. ANN performance gives maximum results in dealing with problems such as classification and prediction [38], [39]. Based on the concept that has been explained that ANN can do learning by adopting a mathematical calculation process [40].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Deep learning approach analysis model prediction and classification poverty status

Yanto

Wiyandra²,

Defit³

2023

IJ-AI

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The problem of poverty is a scourge for every developing country coupled with the economic crisis that occurred during the COVID-19 pandemic. The impact of these problems is felt directly by the people in Indonesia, especially in the Province of West Sumatra. This study aims to predict and classify the level of poverty status by developing an analytical model based on the deep learning (DL) approach. The methods used in this study include the K-Means method, artificial neural network (ANN), and support vector Machine (SVM). The analytical model will be optimized using the Pearson Correlation (PC) method to measure the accuracy of the analysis. The variable indicator uses the parameters of population (X1), poverty rate (X2), income (X3), and poverty percentage (X4). The results of the study present prediction and classification output with a validity level of accuracy of 99.8%. Based on these results, it can be concluded that the proposed DL analysis model can present an updated analytical model that is quite effective in carrying out the prediction and classification process. The research findings also contribute to the initial handling of the problem of poverty.

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“…ANN is a technique or a method developed by adopting a neural network to carry out the analysis process [37]. ANN is able to present effective outputs to deal with certain problems [38], [39]. The concept of ANN is able to carry out the learning process based on a model that is built with mathematical calculations [40].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Optimization artificial neural network classification analysis model diagnosis Gingivitis disease

Ariandi

Yanto²,

Jamhur³

et al. 2023

IJEECS

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Gingivitis is a disease that can be caused by the buildup of bacteria and plaque caused by leftover food. This disease can attack anyone, especially children who are not aware of maintaining dental and oral health. This study aims to build and optimize the classification analysis model for the diagnosis of Gingivitis. The classification analysis model was built using the artificial neural network (ANN) method which was optimized using fuzzy logic and the multiple linear regression (MRL) method. Optimization with fuzzy aims to develop a pattern of rules in the detection. The MRL method is also used as a process of measuring analysis patterns to ensure the analytical model presents maximum results. The results study indicate that the optimization of fuzzy and MRL methods provides excellent output. These results are based on the fuzzy output which can provide a pattern of 40 rules. The MRL method is can present the level of correlation of each analysis variable with a significant output having an average value of 94.2%. Based on the results of this study, the analysis model that is optimized with the fuzzy logic method and MRL contributes to maximizing the process of diagnosing Gingivitis.

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Cancer Drug Classification using Artificial Neural Network with Feature Selection

Cited by 7 publications

References 12 publications

Comparison of the Accuracy of Drug User Classification Models Using Machine Learning Methods

Comparison of the Accuracy of Drug User Classification Models Using Machine Learning Methods

Deep learning approach analysis model prediction and classification poverty status

Optimization artificial neural network classification analysis model diagnosis Gingivitis disease

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