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Title |
Stroke Disease Identification System by Using Machine Learning Algorithm |
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Author |
K.Veena Kumari, Mr.K. Siva Kumar, Dr. M.Sreelatha |
| Citation |
Vol. 25 No. 11 pp. 63-70 |
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Abstract |
A stroke is a medical disease where a blood vessel in the brain ruptures, causes damage to the brain. If the flow of blood and different nutrients to the brain is intermittent, symptoms may occur. Stroke is other reason for loss of life and widespread disorder. The prevalence of stroke is high in growing countries, with ischemic stroke being the high usual category. Many of the forewarning signs of stroke can be recognized the seriousness of a stroke can be reduced. Most of the earlier stroke detections and prediction models uses image examination tools like CT (Computed Tomography) scan or MRI (Magnetic Resonance Imaging) which are costly and difficult to use for actual-time recognition. Machine learning (ML) is a part of artificial intelligence (AI) that makes software applications to gain the exact accuracy to predict the end results not having to be directly involved to get the work done. In recent times ML algorithms have gained lot of attention due to their accurate results in medical fields. Hence in this work, Stroke disease identification system by using Machine Learning algorithm is presented. The ML algorithm used in this work is Artificial Neural Network (ANN). The result analysis of presented ML algorithm is compared with different ML algorithms. The performance of the presented approach is compared to find the better algorithm for stroke identification. |
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Keywords |
AI, Stroke predictions, random forest, Machine Learning. |
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Title |
Estimation and Management of Post-Miscarriage Depression in Women Using Fuzzy Logic and Genetic Algorithm |
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Author |
Fahmida Naveed, Lahore Pakistan, Mehreen Khawar, Dr. Muhammad Rizwan, Dr. Fahad Ahmad, Dr. Kashaf Junaid |
| Citation |
Vol. 25 No. 11 pp. 63-70 |
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Abstract |
At present, a vast amount of people are suffering from mental health disorders. Mental health problems are not taken as seriously as the physical health problems. With the growing rate of miscarriages occurring, the different styles and mechanisms miscarrying-women adopt in order to overcome their grief are explored and used by an expert system based on Fuzzy Logic. The proposed method would estimate the severity of post-miscarriage depression in women using fuzzy systems and genetic algorithms. A dataset would be generated by interviewing the women suffering from post-miscarriage depression. The severity of the depression would be predicted using the variables such as the condition of the women interviewed. Fuzzy logic uses interpretability whereas genetic algorithms along with its five phases makes the production of fuzzy systems automatic and certain coping mechanisms would also be discussed which will help lower the rate of severity of depression in women. The genetic-fuzzy approach gives us optimized results. |
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Keywords |
fuzzy logic, genetic algorithm, coping mechanisms, membership functions, post-miscarriage depression |
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