Implementasi Data Mining Untuk Memprediksi Penyakit Jantung Mengunakan Metode Naive Bayes

Abstract

<jats:p>Heart disease is a disease with a high mortality rate in the world of health. The disease is usually rarely realized the cause. However, there are several parameters that can be used to predict whether a person has a risk of heart disease or not. As for this study, researchers will use several indicators including Age, Sex, Chest pain type, Trestbps, Cholesterol, Fasting blood sugar, Resting ECG, Max heart rate, Exercise-induced angina, Oldpeak, Slope, Number of vessels coloured, and Thal This research will perform calculations using the Data Mining method with the Naive Bayes Algorithm. The results of this study get an accuracy of 86% for the 303 datasets tested. </jats:p>

Coronary Artery Disease Prediction Using Decision Trees and Multinomial Naïve Bayes with k-Fold Cross Validation

“…The results of the discretization of the five numerical variables presented in Table 3 show that each age variable ( 1 ), blood pressure at rest ( 4 ), and cholesterol levels ( 5 ) has three categories, while each variable maximum heart rate ( 8 ) and oldpeak or ST segment obtained from exercise relative to rest ( 10 ) has two categories. The results of this discretization are different from those in Purushottam et al, (2016), David and Belcy, (2018), Riani et al, (2019) (only the variable 10 is the same), as well as Chowdary et al (2020). This study also did not involve missing data like Purushottam et al, (2016).…”

“…Likewise, when compared with Chowdary et al (2020) who obtained accuracy, sensitivity, specificity, and precision of 89%, 86%, 91%, and 91.6%, respectively, in predicting coronary artery disease using the VLRNAK ensemble method. Several other studies using the same dataset as Normawati and Winiarti, (2017), Retnasari and Rahmawati, (2017), Indrajani et al, (2018), Aini et al, (2018), Aulia, (2018, Riani et al, (2019), andPangaribuan et al, (2019) have accuracy that is not better than our work.…”

“…This study discusses risk prediction for coronary artery disease, which can also be called early detection of heart disease, by implementing two statistical learning methods, namely Multinomial Naïve Bayes and Decision Trees with 10-fold cross-validation as a model validation technique. The novelty in this study is a technique for categorizing five numerical variables in research data, namely age (years), cholesterol levels (mg/dl), fasting blood sugar levels (mg/dl), maximum heart rate (bpm), and old peak (mV) conducted with different criteria from Purushottam et al, (2016), as well as David and Belcy, (2018) and Riani et al, (2019).…”

Dampak SMOTE terhadap Kinerja Random Forest Classifier berdasarkan Data Tidak seimbang

“…Pemanfaatan teknik ini dalam berbagai disiplin ilmu telah berkembang dan menunjukkan kontribusi pada ilmu pengetahuan termasuk dalam bidang kesehatan dan kedokteran. Beberapa algoritma machine learning yang paling umum digunakan dalam pemodelan prediksi medis diantaranya Deep Learning [4], algoritma C4.5 [5], Naïve Bayes [6], Support Vector Machine [7], Artificial Neural Network [8], Logistic Regression [9], dan Random Forest [10,11]. Sebagian besar teknik dan algoritma pemodelan ini bekerja dengan sangat baik ketika distribusi kelas dalam dataset terdistribusi secara merata.…”

Sistem Pakar dalam Mengidentifikasi Gejala Stroke Menggunakan Metode Naive Bayes

“…Diketahui algoritma metode Naive Bayes memiliki 4 kemungkinan hasil output. Hal itu berdasarkan pengujian metode Naive Bayes menggunakan dataset sebanyak 303 maka didapatkan hasil akurasi sebesar 86% [10].…”