Penerapan Algoritma Naïve Bayes Untuk Klasifikasi Penyakit Diabetes Mellitus

Diabetes Mellitus atau kencing manis adalah penyakit metabolisme disebabkan oleh kadar gula tinggi didalam darah. Gula darah disimpan atau digunakan untuk energi yang berasal dari darah yang dipindahkan ke sel manusia oleh hormon insulin . ketika terserang Diabetes, pada tubuh manusia insulin tidak biasa dihasilkan secara cukup bahkan tubuh tidak dapat menggunakan insulin tersebut secara benar sesuai kebutuhan. Diabetes Mellitus terdaftar sebagai penyakit penyumbang kematian terbesar terbesar didunia. Diabetes Mellitus dapat diklasifikasikan berdasarkan kemungkinan terkenanya dari atribut gejala diawal fasenya. penyakit ini bisa dideteksi karena banyak gejala yang terdeteksi. Data yang digunakan pada analisis ini merupakan data dari dataset UCI Machine Learning yaitu Early Stage Diabetes Risk tahun 2020 dan terdiri 17 attribut. Analisis yang dilakukan meliputi data preprocessing, model, dan evaluasi. Pengujian Metode klasifikasi pada riset adalah Naïve Bayes Classification. Hasil klasifikasi menunjukkan akurasi sebesar 90.20% dan nilai AUCnya yaitu 0,95

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Prediction of Heart Disease using Machine Learning

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1081.0982s1019 ◽

2019 ◽

Vol 8 (2S10) ◽

pp. 474-477

Keyword(s):

Machine Learning ◽

Heart Disease ◽

Support Vector Machines ◽

Naive Bayes ◽

Naïve Bayes ◽

Support Vector ◽

Data Set ◽

Vector Machines ◽

Naive Bayes Classification ◽

Naïve Bayes Classification

Machine learning is one of the fast growing aspect in current world. Machine learning (ML) and Artificial Neural Network (ANN) are helpful in detection and diagnosis of various heart diseases. Naïve Bayes Classification is a vital approach of classification in machine learning. The heart disease consists of set of range disorders affecting the heart. It includes blood vessel problems such as irregular heart beat issues, weak heart muscles, congenital heart defects, cardio vascular disease and coronary artery disease. Coronary heart disorder is a familiar type of heart disease. It reduces the blood flow to the heart leading to a heart attack. In this paper the UCI machine learning repository data set consisting of patients suffering from heart disease is analyzed using Naïve Bayes classification and support vector machines. The classification accuracy of the patients suffering from heart disease is predicted using Naïve Bayes classification and support vector machines. Implementation is done using R language.

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Novel naïve Bayes classification models for predicting the carcinogenicity of chemicals

Food and Chemical Toxicology ◽

10.1016/j.fct.2016.09.005 ◽

2016 ◽

Vol 97 ◽

pp. 141-149 ◽

Cited By ~ 26

Author(s):

Hui Zhang ◽

Zhi-Xing Cao ◽

Meng Li ◽

Yu-Zhi Li ◽

Cheng Peng

Keyword(s):

Naive Bayes ◽

Naïve Bayes ◽

Classification Models ◽

Naive Bayes Classification ◽

Naïve Bayes Classification

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Adaptive sensing scheme using naive Bayes classification for environment monitoring with drone

International Journal of Distributed Sensor Networks ◽

10.1177/1550147718756036 ◽

2018 ◽

Vol 14 (1) ◽

pp. 155014771875603 ◽

Cited By ~ 2

Author(s):

Yao-Hua Ho ◽

Yu-Te Huang ◽

Hao-Hua Chu ◽

Ling-Jyh Chen

Keyword(s):

Environmental Changes ◽

Naive Bayes ◽

Cost Effective ◽

Parameter Tuning ◽

Naïve Bayes ◽

Spatial Distance ◽

Environmental Sensors ◽

Adaptive Sensing ◽

Naive Bayes Classification ◽

Naïve Bayes Classification

Environmental sensors are important for collecting data to understand environmental changes and analyze environmental issues. In order to effectively monitor environmental changes, high-density sensor deployment and evenly distributed spatial distance between sensors become the requirements and desired properties for such applications. In many applications, sensors are deployed in locations that are difficult and dangerous to reach (e.g. mountaintop or skyscraper roof). To collect data from those sensors, unmanned aerial vehicles are used to act as data mules to overcome the problem of collecting data in challenging environments. In this article, we extend the adaptive return-to-home sensing algorithm with a parameter-tuning algorithm that combines naive Bayes classification and binary search to adapt adaptive return-to-home sensing parameters effectively on the fly. The proposed approach is able to (1) optimize number of sensing attempts, (2) reduce oscillation of the distance for consecutive attempts, and (3) reserve enough power for drone to return-to-home. Our results show that the naive Bayes classification–enhanced adaptive return-to-home sensing scheme is able to avoid oscillation in sensing and guarantees return-to-home feature while behaving more cost-effective in parameter tuning than the other machine learning–based approaches.

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