Klasifikasi Pasien Kanker Payudara Menggunakan Metode Support Vector Machine dengan Backward Elimination

Resmiati, Rina; Arifin, Toni

doi:10.32520/stmsi.v10i2.1238

Cited by 6 publications

(5 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Selanjutnya ada salah satu metode yang dapat digunakan untuk melakukan suatu deteksi dini pada kanker tersebut ialah menggunakan suatu teknik machine learning [6]. Dan dengan hal ini untuk meminimalisir kesalahan dalam pendeteksian juga menghindari keterlambatan deteksi pada sel kanker maka dapat dilakukan suatu penerapan dan juga pemanfaatan teknik data mining [7].…”

Section: Pendahuluanunclassified

Prediksi Keganasan Kanker Payudara Dengan Pendekatan Machine Learning

Lestari,

Isti Rahayu

2023

jati

View full text Add to dashboard Cite

Penelitian ini adalah sebuah laporan yang mengulas prediksi dan klasifikasi kanker payudara dengan tujuan untuk mengeksplorasi bagaimana prediksi dan klasifikasi kanker payudara telah dilakukan dalam lima tahun terakhir dan mencari model dengan tingkat akurasi tinggi. Ragam variasi data menjadi informasi berharga dalam klasifikasi dan prediksi kanker payudara. Dilakukan sebuah tinjauan literatur sistematis dengan mengkaji artikel-artikel menggunakan alat "publish or perish" dan kata kunci "breast cancer classification" + "machine learning", serta menerapkan kriteria inklusi dan eksklusi. Terdapat 44 artikel yang telah dikumpulkan, kemudian disaring dengan menerapkan kriteria inklusi dan eksklusi sehingga terpilih 14 artikel yang termasuk dalam kategori artikel terpilih. Hasilnya, metode yang banyak digunakan adalah deep learning, dengan dataset kanker payudara Wisconsin yang sering digunakan dalam penelitian ini. Evaluasi kinerja yang umum digunakan meliputi akurasi, presisi, recall, F1-score, dan AUC. Model dengan tingkat akurasi tinggi adalah jaringan saraf dalam deep learning dengan tingkat akurasi sebesar 99,7%.

show abstract

Section: Pendahuluanunclassified

Prediksi Keganasan Kanker Payudara Dengan Pendekatan Machine Learning

Lestari,

Isti Rahayu

2023

jati

View full text Add to dashboard Cite

show abstract

“…Support Vector Machine (SVM) is a Machine Learning algorithm that is included in the Supervised Learning model or supervised learning related to data analysis and pattern recognition [11] and is a suitable method for classification and regression problems, both linear and nonlinear [18]. The SVM concept can be expressed as an attempt to find an optimal classifier/hyperplane function that can separate two data sets from two different classes [7].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Exploratory Data Analysis of Clinical Heart Failure Using a Support Vector Machine

Sinaga,

Purba,

Wiranto

et al. 2023

JUSIKOM PRIMA

View full text Add to dashboard Cite

This study aims to explore the clinical data of patients diagnosed with heart failure using the Support Vector Machine (SVM) algorithm as a classification method. Clinical data from verified patients has been collected and analyzed to identify patterns, associations, and risk factors contributing to heart failure risk. The exploratory data analysis results reveal essential clinical data characteristics and provide initial insight into patient profiles and clinical variables that can influence heart failure risk. The SVM model was built to predict the risk of heart failure based on clinical data. This model is evaluated using classification metrics such as F1-Score and accuracy. Evaluation results show good performance with an F1-Score reaching 0.83, which indicates a reasonable degree of accuracy and balance in predicting the risk of heart failure. The conclusion of this study shows the potential of the classification model as a tool in managing heart failure patients. This model can help medical personnel identify high-risk patients and provide appropriate treatment to prevent disease progression and improve prognosis. However, these results need further verification with more in-depth analysis and validation using broader data. This model can help medical personnel identify high-risk patients and provide appropriate treatment to prevent disease progression and improve prognosis. However, these results need further verification with more in-depth analysis and validation using broader data. This model can help medical personnel identify high-risk patients and provide appropriate treatment to prevent disease progression and improve prognosis. However, these results need further verification with more in-depth analysis and validation using broader data. Keywords: Exploratory Data Analysis, Heart Failure, Classification, Python, Support Vector Machine

show abstract

“…Research (Resmiati & Arifin, 2021) proposes Backward Elimination feature selection to improve the SVM method for breast cancer classification. The results show that using the Backward Elimination feature selection significantly improves SVM performance with an accuracy of 95% on breast cancer classification.…”

Section: Introductionmentioning

confidence: 99%

SMOTE Variants and Random Forest Method: A Comprehensive Approach to Breast Cancer Classification

Baiq Candra Herawati,

Hairani Hairani,

Juvinal Ximenes Guterres

2024

IJEC

View full text Add to dashboard Cite

This research focused on using machine learning methods for breast cancer diagnosis, considering that breast cancer is the scariest disease for women because it can cause mortality. Not only that, but there is also an increase in breast cancer death rates in women yearly. Early prediction is the right solution to increase life expectancy and reduce mortality rates caused by breast cancer. However, breast cancer data has a problem, namely that the data is imbalanced, which harms the performance of the machine learning method itself. In the data, breast cancer had a Benign class (357 instances) more than the Malignant class (212 instances). Therefore, this study aimed to solve the problem of imbalanced data using the Smote variants and Random Forest approaches in breast cancer classification. The results of this study showed that the Smote approach with Random Forest had the best performance compared to Borderline Smote and Random Forest in the case of breast cancer data classification, where Smote with Random Forest produced an accuracy of 97.3%, sensitivity of 96.9%, and specificity of 97.8%. In comparison, Borderline Smote with Random Forest produced an accuracy of 96.4%, sensitivity of 95.6%, and specificity of 96.9%. The results of this study can contribute to predicting breast cancer using the proposed method, because it has been proven to have high accuracy.

show abstract

Klasifikasi Pasien Kanker Payudara Menggunakan Metode Support Vector Machine dengan Backward Elimination

Cited by 6 publications

References 16 publications

Prediksi Keganasan Kanker Payudara Dengan Pendekatan Machine Learning

Prediksi Keganasan Kanker Payudara Dengan Pendekatan Machine Learning

Exploratory Data Analysis of Clinical Heart Failure Using a Support Vector Machine

SMOTE Variants and Random Forest Method: A Comprehensive Approach to Breast Cancer Classification

Contact Info

Product

Resources

About