منابع مشابه
Predicting Phospholipidosis Using Machine Learning
Phospholipidosis is an adverse effect caused by numerous cationic amphiphilic drugs and can affect many cell types. It is characterized by the excess accumulation of phospholipids and is most reliably identified by electron microscopy of cells revealing the presence of lamellar inclusion bodies. The development of phospholipidosis can cause a delay in the drug development process, and the impor...
متن کاملIntelligent application for Heart disease detection using Hybrid Optimization algorithm
Prediction of heart disease is very important because it is one of the causes of death around the world. Moreover, heart disease prediction in the early stage plays a main role in the treatment and recovery disease and reduces costs of diagnosis disease and side effects it. Machine learning algorithms are able to identify an effective pattern for diagnosis and treatment of the disease and ident...
متن کاملImproving the Performance of Machine Learning Algorithms for Heart Disease Diagnosis by Optimizing Data and Features
Heart is one of the most important members of the body, and heart disease is the major cause of death in the world and Iran. This is why the early/on time diagnosis is one of the significant basics for preventing and reducing deaths of this disease. So far, many studies have been done on heart disease with the aim of prediction, diagnosis, and treatment. However, most of them have been mostly f...
متن کاملPredicting Bankruptcy Using Machine Learning Algorithms
This paper is written for predicting Bankruptcy using different Machine Learning Algorithms. Whether the company will go bankrupt or not is one of the most challenging and toughest question to answer in the 21 st Century. Bankruptcy is defined as the final stage of failure for a firm. A company declares that it has gone bankrupt when at that present moment it does not have enough funds to pay t...
متن کاملPredicting Increased Blood Pressure Using Machine Learning
The present study investigates the prediction of increased blood pressure by body mass index (BMI), waist (WC) and hip circumference (HC), and waist hip ratio (WHR) using a machine learning technique named classification tree. Data were collected from 400 college students (56.3% women) from 16 to 63 years old. Fifteen trees were calculated in the training group for each sex, using different num...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Physics: Conference Series
سال: 2021
ISSN: 1742-6588,1742-6596
DOI: 10.1088/1742-6596/1916/1/012208