Platelet Indices as a marker for early prediction of erectile dysfunction in diabetic patients

Andrologia ◽  
2018 ◽  
Vol 51 (1) ◽  
pp. e13163 ◽  
Author(s):  
Moustafa A. El Taieb ◽  
Eisa M. Hegazy ◽  
Salah M. Maklad ◽  
Reham Khairy
Keyword(s):  
Erectile Dysfunction ◽  
Diabetic Patients ◽  
Early Prediction ◽  
Platelet Indices

The relationship between serum irisin levels and erectile dysfunction in diabetic men (irisin and erectile dysfunction in diabetic patients)

Andrologia ◽  
2021 ◽  
Author(s):  
Şükrü Kumsar ◽  
Özlem Ciğerli ◽  
Eray Hasırcı ◽  
Ali F. Akay ◽  
Levent Peşkircioğlu
Keyword(s):  
Erectile Dysfunction ◽  
Diabetic Patients ◽  
The Relationship

scholarly journals Relation Between Erectile Dysfunction and Silent Myocardial Ischemia in Diabetic Patients: A Multidetector Computed Tomographic Coronary Angiographic Study

2016 ◽  
Vol 4 (3) ◽  
pp. e127-e134 ◽  
Author(s):  
Amr Abdelhamed ◽  
Shin-ichi Hisasue ◽  
Essam A. Nada ◽  
Ali M. Kassem ◽  
Mohammed Abdel-Kareem ◽  
...  
Keyword(s):  
Erectile Dysfunction ◽  
Myocardial Ischemia ◽  
Silent Myocardial Ischemia ◽  
Diabetic Patients ◽  
Angiographic Study ◽  
Computed Tomographic

Safety and Efficacy of Alprostadil Sterile Powder (S. Po., CAVERJECT®) in Diabetic Patients with Erectile Dysfunction

2000 ◽  
Vol 38 (2) ◽  
pp. 177-183 ◽  
Author(s):  
Yuh-Shyan Tsai ◽  
Johnny Shinn-Nan Lin ◽  
Yung-Ming Lin
Keyword(s):  
Erectile Dysfunction ◽  
Diabetic Patients ◽  
Safety And Efficacy

scholarly journals Early Prediction Model for Type-2 Diabetes Based on Lifestyle

2020 ◽  
Vol 32 ◽  
pp. 03053
Author(s):  
Kirti Hirnak ◽  
Nikita Chaudhari ◽  
Akshay Singh ◽  
Deepali Patil
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Deep Learning ◽  
Chronic Disease ◽  
Diabetic Patients ◽  
Early Prediction ◽  
Learning Approaches ◽  
Random Forest Algorithm ◽  
Blurry Vision

Nowadays diabetes has become a chronic disease that may cause many complications. There are some symptoms of diabetes such as increased appetite, blurry vision, and extreme fatigue, etc. As the increasing deformity in present years the number of diabetic patients from the whole world will reach to 642 million. Diabetes accuracy is very difficult to know so in order to cure this disease. These causes us to concentrate more there to make some changes that will reduce these numbers. So to minimize these numbers of diabetes, we researched various algorithms and methods. The proposed method focuses on extracting the attributes that gives a result in early detection of Diabetes Mellitus in patients. Various existing processes provide just a result as the patient has diabetes or not which will require the patients to visit a diagnostic centers or to a doctor. So we proposed a system based on deep learning approaches that will help to solve a serious problem. These systems take collaborative inputs from dataset to give prediction with random forest algorithm which gives more accurate results.

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