ANN for Predicting DNA Lung Cancer

Abstract: Lung cancer is the top reason of cancer-associated deaths globally. Surgery is the typical treatment for early-stage non-small cell lung cancer (NSCLC). Advancement in the knowledge of the biology of non-small cell lung cancer has shown molecular evidence used for systemic cancer therapy aiming metastatic disease, with a significant impact on patients’ overall survival (OS) and eminence of life. Though, a biopsy of overt metastases is an invasive technique restricted to assured positions and not effortlessly satisfactory in the clinic. The examination of peripheral blood samples of cancer patients embodies a new basis of cancer-derived material, recognized as liquid biopsy, and its constituents (circulating tumour cells (CTCS), circulating free DNA (cfDNA), exosomes, and tumour-educated platelets (TEP)) may be gotten from nearly any body liquids. These constituents have shown to imitate features of the status of both the primary and metastatic diseases, aiding the clinicians to go towards a tailored medicine. In this paper, the reasons of lung cancer will be recognized and the risk elements that initiated the increase of infection, for instance Smoking, Disclosure to secondhand smoke, Disclosure to radon gas, Disclosure to asbestos and other compounds, Family past history of lung cancer, and decrease of the spread of disease and approaches of handling and prevention of lung cancer

Diabetes Prediction Using Artificial Neural Network

Diabetes is one of the most common diseases worldwide where a cure is not found for it yet. Annually it cost a lot of money to care for people with diabetes. Thus the most important issue is the prediction to be very accurate and to use a reliable method for that. One of these methods is using artificial intelligence systems and in particular is the use of Artificial Neural Networks (ANN). So in this paper, we used artificial neural networks to predict whether a person is diabetic or not. The criterion was to minimize the error function in neural network training using a neural network model. After training the ANN model, the average error function of the neural network was equal to 0.01 and the accuracy of the prediction of whether a person is diabetics or not was 87.3

A Survey on Data Mining Techniques for Prediction of Heart Diseases

International audienceHeart disease (HD) is a disease of the heart or blood vessels, which causes death. In recent scenario, health issues are huge, due to this nature predicting and classifying into different conditions are very tedious. The field of data mining has involved in those domains to predict and to classify the abnormality along with its risk level. The previous studies have used several features to diagnosis the disease, which has been collected from patients. By applying different data mining algorithms, the patient data can be used for diagnosis as training samples. The main drawbacks of the previous studies are that need accurate and more number of features. This paper surveys about the recent data mining techniques applied for predicting heart diseases

Rule-based Disease Classification using Text Mining on Symptoms Extraction from Electronic Medical Records in Indonesian

Recently, electronic medical record (EMR) has become the source of many insights for clinicians and hospital management. EMR stores much important information and new knowledge regarding many aspects for hospital and clinician competitive advantage. It is valuable not only for mining data patterns saved in it regarding the patient symptoms, medication, and treatment, but also it is the box deposit of many new strategies and future trends in the medical world. However, EMR remains a challenge for many clinicians because of its unstructured form. Information extraction helps in finding valuable information in unstructured data. In this paper, information on disease symptoms in the form of text data is the focus of this study. Only the highest prevalence rate of diseases in Indonesia, such as tuberculosis, malignant neoplasm, diabetes mellitus, hypertensive, and renal failure, are analyzed. Pre-processing techniques such as data cleansing and correction play a significant role in obtaining the features. Since the amount of data is imbalanced, SMOTE technique is implemented to overcome this condition. The process of extracting symptoms from EMR data uses a rule-based algorithm. Two algorithms were implemented to classify the disease based on the features, namely SVM and Random Forest. The result showed that the rule-based symptoms extraction works well in extracting valuable information from the unstructured EMR. The classification performance on all algorithms with accuracy in SVM 78% and RF 89%

Tuberculosis Disease Forecasting Among Indian Patients

Tuberculosis is a conspicuous syndrome for all individuals in developing countries including India. It is an uttermost causation of bereavement in personage. It is an ailment triggered by bacteria which strikes hominid body parts, primarily lungs. The desideratum of this paper is to foretell tuberculosis disease using data mining techniques, which tends to make a medical diagnosis of tuberculosis rigorous. Data Mining Techniques will help to glean that whether it is plausible to start tuberculosis treatment on suspected victims or not, without waiting for pernickety medical test outcomes. This scrutiny emphasis on patients health and provides treatment at low outlay through forecasting systems. There are assorted parameters such as Cough, Chest Pain, Night Sweats, Age, Weight Loss, Gender and Fever, Coughing up Blood, No Appetite which are used for predicting tuberculosis. Both Genetic algorithm and Neural network backwash better than other techniques. Tuberculosis disease forecasting is accomplished by soft computing technique. Genetic algorithm offers best fitness value, disembroil optimization problems whereas Neural Network takes parameters as input and also utilize genetic operators to train the neural network and spawn an output for presaging tuberculosis disease. This research outlines the main review and technical papers on tuberculosis detection that are implemented using multifarious data mining techniques. Review of papers surmises that soft computing technique acquires the highest accuracy