Shashank Mahajan, Gaurav Shrivastava / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 4, Jul-Aug 2013, pp.2229-2231 2229 | P a g e Effective Diagnosis of Diseases through Symptoms Using Artificial Intelligence and Neural Network Shashank Mahajan*, Gaurav Shrivastava** *(Research Scholar, Department of Information Technology, SVITS,Indore) ** (Asst. Professor, Department of Information Technology, SVITS,Indore) ABSTRACT In this Research paper is based on artificial intelligence. Artificial Intelligence means learn by knowledge. In this research mechanism for artificial doctor that based on knowledge based. This artificial doctor has the capability to give possibilities of all diseases on the basis of symptoms of patient. It’s like an assistant doctor with more intelligent’s. This mechanism asks the patient about the symptoms. On the basis of those symptoms it will suggest about the possibilities of diseases. This mechanism helps to doctor to identify the disease of the patient. It will also ask about the previous and family history. This mechanism gave the result by studying the previous treatment also, so it takes every possibility of diseases. And it will also alert the doctor for the medicine which cannot be given to the patient. Keywords – Artificial Neural Networks, General Disease Diagnosis, Medical Diagnosis, Medical Knowledge, Neural Networks I. INTRODUCTION MEDICAL diagnosis always has been an art: we remember famous doctors as well as famous painters or composers throughout the history. Again, who is called an artist? A person who can carry out something those others cannot, and that is exactly what a good physician does during a medical diagnosis procedure. He (or she) employs his educations, experiences, and talent, to diagnose a disease. A diagnosis procedure usually starts with the patient complaints and the doctor learn more about the patient situation interactively during an interview, as well as by measuring some metrics such as blood pressure or the body temperature. The diagnosis is then determined by taking the whole available patients status into the account. [1] AI doctors different from human doctors but they aren’t limited by human capacity. AI doctors, having millions of points of data to draw on, will remember where a human doctor might forget. Because of this, they are in theory designed for more accurate diagnosis. AI doctors also do not need to sleep or eat and do not get sick themselves. This means they can work 24/7, stay alert the whole time and may cut costs for the hospital they work. II. LITERATURE SURVEY 1.1 Hypertension is a disease that affects a wide range of the population, particularly the elderly after the age of 55. Hypertension is caused by Blood Pressure. Blood Pressure is the force of blood pushing against blood vessel walls. The heart pumps blood into the arteries (blood vessels), which carry the blood throughout the body. If blood pressure is extremely high, there may be certain symptoms such as Severe headache, Fatigue, disorientation, Vision problems, Chest pain, Difficulty in breathing, irregular heartbeat and Blood in the urine . Hypertension can cause Stroke, Heart failure, Heart attack, Kidney failure and Vision problems. Men have a greater likelihood of developing high BP than women. This varies according to age and among various ethnic groups. In some cases, computer-based assisted diagnoses have been claimed to be even more accurate than those by clinicians. Predicting the outcome of it is one of the most interesting and challenging tasks in which a Neural Network application is developed. Neural Networks are well suited to problems that people use good at solving but for which computers are not. Neural Networks provide a very general way of approaching problems.[2] 1.2 The diagnosis of diseases is a vital and intricate job in medicine. The recognition of heart disease from diverse features or signs is a multi-layered problem that is not free from false assumptions and is frequently accompanied by impulsive effects. Thus the attempt to exploit knowledge and experience of several specialists and clinical screening data of patients composed in databases to assist the diagnosis procedure is regarded as a valuable option. This research work is the extension of our previous research with intelligent and effective heart attack prediction system using neural network. A proficient methodology for the extraction of significant patterns from the heart disease warehouses for heart attack prediction has been presented. Initially, the data warehouse is pre-processed in order to make it suitable for the mining process. Once the preprocessing gets over, the heart disease warehouse is clustered with