Computer-Aided Medical Diagnosis System Using Logistics Regression Algorithms (LRA) Supervised Learning Approach

This work focused on the designing of medical diagnosis system using Supervised Machine Learning. Logistics Regression Algorithms (LRA) was adopted, the label inputs for the data set which the symptoms were trained and mapped with the input of the user. Diagnosis of malaria was considered in this work; the system verified the value of the logical regression in the medical decision support system. Medical practitioners and other health workers can use this system to make better decisions in medical diagnosis for malaria. Adoption of this system will reduce stress of diagnoses malaria from patient and reduce congestion in our hospitals.

STRESS PREDICTION USING RANDOM FOREST LINEAR REGRESSION

In today’s world stress has become a more familiar word because of its disastrous impact on the huge number of people worldwide. It is very important to keep stress under control every time, as it is the primitive reason for much major health issues. Some people meditate to g e t r i d o f i t and others choose to use medicines to control their stress levels. Students also found with very much stressed out because of academics, projects, exams, and whatnot. There are many ways through which one can check whether you have stress or not. According to this situation, the medical diagnosis system based on human physiology becomes more requisite as compared to others. Human physiology-based study plays a important character in the detection of mental stress in persons. There have also been eventual researches which are done on the detection of stress based on facial emotions. To find out whether stressed or not we need to see a doctor and get checked, but it seems to be not practical at all times to do so. In fact, in the era of digitalism, where everyone has a smartphone there is a dearth of finding novel ways through which we can make use of technology to detect your stress levels automatically. There are wearable devices that detect stress levels based on your body activity. Many approaches aim for the detection of stress through the use of wearable devices. The approach that we are presenting in this project is predicting stress through medical data of the patients using random forest regression. Additionally, an examination between oneself fabricated convolution neural model and a portion of the pre-trained models has been finished. This is another methodology and we are getting very promising precision by utilizing sufficient research experiments on 2000 irregular trees in the model. The results achieved are the outcomes of effectively anticipated with the accuracy utilizing the model. The outcomes of this research can be useful in directing the future which explor

Survey on Heart Disease Anticipation Through Genetic Algorithm and BP-Neural Network

Medical Diagnosis Systems play a vital role in medical practice and are used by medical practitioners for diagnosis and treatment. In this paper, a medical diagnosis system is presented for predicting the risk of cardiovascular disease. This system is built by combining the relative advantages of genetic algorithm and neural network. Multilayered feed forward neural networks are particularly suited to complex classification problems. The weights of the neural network are determined using genetic algorithm because it finds acceptably good set of weights in less number of iterations. The dataset provided by University of California, Irvine (UCI) machine learning repository is used for training and testing. It consists of 303 instances of heart disease data each having 14 attributes including the class label. First, the dataset is preprocessed in order to make them suitable for training. Genetic based neural network is used for training the system. The final weights of the neural network are stored in the weight base and are used for predicting the risk of cardiovascular disease. The classification accuracy obtained using this approach is 94.17%.