Heart Disease Detector Web App
May 19, 2021Announcements / Computer Science / Machine Learning / Projects Yes, you heard that right. We are back with more Machine Learning content and another web app (Yup, machine learning is definitely very cool). This app is similar to our first web app i.e. Diabetes Detector. This is another disease detection web app which detects if you have any cardiovascular diseases (CVD) based on certain features such as age, resting blood pressure, cholestrol, maximum heart rate achieved, ST depression induced by exercise and number of major vessels coloured by flouroscopy. ST depression refers to a finding on an electrocardiogram, wherein the trace in the ST segment is abnormally low below the baseline. Fluoroscopy is an imaging technique that uses X-rays to obtain real-time moving images of the interior of an object. This app is another example of the use of machine learning and data science in the field of medicine which I find very innovative. This web app is based on machine learning and uses the Random Forest Classifier algorithm. The web app uses interactive visual and graphical interpretations to display the outcome and compare the input parameters given by the user. The sidebar sliders help in changing the values of the parametres for determination of the result. The graphs compare the values of the patient with others ( both with patients having heart diseases and not having heart diseases). It also provides the accuracy of the result. This is just a learning project based on one particular dataset so please do not depend on it to actually know if you have a heart disease or not. It might still be a false positive or false negative. A doctor is still the best fit for the determination of such diseases. We have deployed the app using Streamlit. It is an open source framework that allows data science teams to deploy web apps fairly easily. It’s one of the best hosting services I’ve used and it’s great for quick and easy deployment of web apps. The app is coded in python. This web app helped build upon my experience and comfort over machine learning and its vast applications. It is one of our many projects in the field of computer science and specifically data science. We hope to deploy more articles in the future and share them with you. Please feel free to add on and contribute to the project and don’t hesitate to drop any suggestions. The link for the web app is: https://share.streamlit.io/pranav-coder2005/heartdisease/main/app.py Disclaimer: This is just a learning project based on one particular dataset so please do not depend on it to actually know if you have any cardiovascular disease or not. It might still be a false positive or false negative. A doctor is still the best fit for the determination of such diseases. The awareness month for heart health in the United States is February. National wear red day is observed on the first Friday of February of each year to increase awareness about heart diseases. World Heart Federation, World Health Organization and many other such organizations run campaigns dedicated to the prevention, diagnosis and control of cardiovascular diseases.The World Heart Federation is a nongovernmental organization based in Geneva, Switzerland, formed in 1972 In 1978 the International Society of Cardiology merged with the International Cardiology Federation to form the International Society and Federation of Cardiology. The federation hosts the World Congress of Cardiology. World Heart Day” was founded in 2000 to inform people around the globe that heart disease and stroke are the world’s leading causes of death. World Heart Day is celebrated every year on 29 September. On that note, let’s increase cardiovascular health awareness and make the world much healthier. ... Read more...
Penguin Classification and Prediction Web App
May 29, 2021Announcements / Computer Science / Machine Learning / ProjectsAfter a friend told me about the Streamlit library, I instantly started working on multiple data science related projects which not only increased my abilities in data science, but made me more adept with the use of programming languages such as Python, MATLAB and HTML. At the time of writing this article, users can only deploy 3 web apps on Streamlit from a particular account. But, if you request them you can potentially also deploy more web apps. So coming onto this project, it’s similar to the Iris Classification and Prediction Web App and this dataset is used as an alternative to the classic Iris dataset given by R.A. Fisher. It’s a penguin classification and prediction web app. It successfully classifies and predicts the species of Palmer penguin based upon a number of user input features such as bill length, bill depth, body mass, flipper length etc. The prediction is also based upon the gender of the penguin and the island group (Bisoce, Dream and Torgersen) where it is located. Just like most of my other web apps, this has a simple design and structure to improve UI/UX for user convenience. The sidebar sliders help in changing the values of the parameters for determination of the result. It also displays the prediction probability along with the predicted output of the Palmer penguin species. This web app also displays the image of the predicted species. We have 3 different images for all three species which are displayed depending upon the predicted species based upon user input of parameter values. We have deployed the app using Streamlit. It is an open source framework that allows data science teams to deploy web apps fairly easily. It’s one of the best hosting services I’ve used and it’s great for quick and easy deployment of web apps. The app is majorly coded in python. This web app helped me to improve my experience in Machine Learning and definitely helped in my future projects. Feel free to add onto this project and don’t hesitate to drop by any suggestions. Hope you enjoy the app! Link of the app: https://share.streamlit.io/skillocity/penguin/main/prediction.py About the dataset: This dataset was created by Dr. Kristen Gorman and members of the Palmer Station, Antarctica (LTER). Palmer is one of the three US Antarctic Stations governed by the Antarctic Treaty of 1959. The Palmer Station is an interdisciplinary polar marine research program established in 1990. The dataset was uploaded by Allison Horst and it is available by CC-0 license in accordance with the Palmer Station LTER Data Policy and the LTER Data Access Policy for Type 1 data. The dataset contains data for 344 penguins. There are 3 diffrent species of penguins in this dataset, collected from 3 islands in the Palmer Archipelago, Antarctica. ... Read more...
Mesothelioma Disease Detector
June 29, 2021UncategorizedI recently found another disease detection dataset on the UC Irvine Machine Learning Repository and decided to have a go at this one. However this dataset is not used that often for disease detection as I have never heard of it before and never even came across it before discovering it while reading a research paper. I’d like to shed some light on this dataset and present the Mesothelioma Disease Detector Web App. This web app detects if you have Mesothelioma or not depending upon various parameters such as Platelet Count, Blood Lactic Dehydrogenise, Alkaline Phosphatise, Total Protein, Albumin, Glucose, Pleural Lactic Dehydrogenise, Pleural Protein, Pleural Albumin, Pleural Glucose and C-reactive Protein. I’ll explain more about these parameters later on in the article. This web app works on the Random Forest Classifier algorithm and is majorly coded in python. Mesothelioma is a type of cancer that occurs in the thin layer of tissue that covers the majority of our internal organs (mesothelium). Mesothelioma is an aggressive and deadly form of cancer. We have deployed the app using Streamlit. It is an open source framework that allows data science teams to deploy web apps fairly easily. It’s one of the best hosting services I’ve used and it’s great for quick and easy deployment of web apps. The app is coded in python. The web app uses interactive visual and graphical interpretations to display the outcome and compare the input parameters given by the user. We trained the dataset using 20% of it as our test size. The sidebar sliders help in changing the values of the parametres for determination of the result. The graphs compare the values of the patient with others ( both with patients having mesothelioma and patients not having mesothelioma). This web app was a learning curve for us and has improved our knowledge about Machine learning significantly. We hope to deploy more apps in the future and share them with you. Feel free to add onto this project and don’t hesitate to drop by any suggestions. The link for the Mesothelioma Disease Detector web app is as follows : https://share.streamlit.io/skillocity/mesothelioma-/main/app.py About the dataset: Malignant mesotheliomas (MM) are very aggressive tumors of the pleura. These tumors are connected to asbestos exposure, However it may also be related to previous simian virus 40 (SV40) infection and quite possible for genetic predisposition.Molecular mechanisms can also be implicated in the development of mesothelioma.Rural living is associated with the development of mesothelioma. Soil mixtures containing asbestos, known asâ€˜white-soilâ€™ or â€˜corakâ€™ can be found in Anatolia, Turkey and â€˜Lutoâ€™ in Greece.Mesotheliomaâ€™s disease data set were prepared at Dicle University Faculty of Medicine in Turkey.Three hundred and twenty-four Mesothelioma patient data. In the dataset, all samples have 34 features. Disclaimer: This is just a learning project based on one particular dataset so please do not depend on it to actually know if you have Mesothelioma or not. It might still be a false positive or false negative. A doctor is still the best fit for the determination of such diseases. Mesothelioma Awareness Day is Sept. 26. On this day, patients, family members, doctors and the mesothelioma community raise awareness of the rare cancer to help find a cure. Supporters wear blue and may wear mesothelioma awareness wristbands or ribbons.t was established in 2004 by the Mesothelioma Applied Research Foundation. It wasn’t until 2010 that Congress first declared September 26 as National Mesothelioma Awareness Day. On that note, lets raise awareness for Mesothelioma and show our support for Mesothelioma awareness and help many patients around the world. ... Read more...

Is the Vision of a colony on Mars before the end of the decade possible?
August 22, 2020Astronomy / Space TechnologyWith the recent 150m hop of Starship prototype SN5, Elon’s ambition (and our desire!) of colonizing Mars in the 2030s looks very real. If the development and testing were to continue at this pace, Starship should be ready for its maiden voyage before 2025. We’d be ready with the capability to launch humans, no doubt, but would we ready with the human systems? A round trip to Mars would roughly take about 21 months: about 18 months in deep space and 3 on Mars. Starship would probably be able to take us to Mars a little faster, possibly, in 6 months. But even that span could cause serious damage to the health of an astronaut if not protected from. So, what is this radiation that we’re talking about? We’re talking about ionizing or high-energy radiation. It includes X-rays, gamma rays, high energy protons, and cosmic radiation which on interaction with a surface would produce secondary radiation(alpha and beta particles) by knocking off electrons! These could be very hazardous especially since the amount of cosmic rays entering our solar system has increased due to the period of shrinking heliosphere, the bubble around our solar system created by solar wind pressure. (more on that in a later article). Oh, and did I give an honorable mention to the good ol’ Coronal Mass Ejections (or CME’s) by our dear Sun? If one is to occur during a deep space mission, the hazards for astronauts would increase by a good factor. Though the Orion capsule would be equipped with the HERA sensor, or Hybrid Electronic Radiation Assessor, to provide a warning if crew members need to take shelter in the middle of the crew module with storage bags as a cover in the case of a radiation event, such as a solar flare. The fear of an instrument failure and/or a partial mission failure remains. For a fact, a solar storm event occurred between Apollo 16 and 17 missions. Had it occurred during one of those, history would have been a little different. Why worry about radiation though? Did we not go to the Moon with minimal protection? Well, yes we did, but the duration of the Apollo missions was roughly 8 days. On Earth, due to its magnetosphere, on average humans experience about 2.4 millisieverts a year(varies based on location). The problems due to radiation are assumed to start at about 2.5 rads or 500 milli SV. But that isn’t accurate enough since radiation measurement accounts for a lot of factors. The radiation the Apollo astronauts experienced averaged at about 1.14 rads or about 10millisieverts. To put this into perspective, an abdominal CT scan exposes you to about 7mSV. Eating a banana exposes you to about 0.1 microsieverts of radiation from radioactive potassium in it. In an airplane at 30,000 feet, the level of radiation is up to 3 microsieverts. Astronauts aboard the ISS receive about 80 millisieverts(or 80,000 micro SV). The minimal protection on Apollo spacecrafts did come with a cost. Due to radiation, Apollo astronauts experienced bright flashes in their eyes due to cosmic rays hitting at the back of the retina creating Cherenkov radiation( a type of radiation caused due to charged particles moving faster than the speed of light in a particular medium) which could have played a part imparting 39 Apollo-era astronauts some form of cataract. Out of these 36 flew on high radiation moon landing missions. According to a study in 2016 by Michael D. Delp and collaborators, the Cardiovascular Disease(CVD) mortality rate among Apollo lunar astronauts is 4-5 times higher than LEO astronauts. The number of deaths due to CVD was almost 5 times higher in Apollo astronauts. Note that several Apollo astronauts also had flown on Skylab, Gemini or Mercury missions. Radiation, specifically high atomic number 56Fe, can also alter cellular physiology through DNA (deoxyribonucleic acid) mutations. Astronauts may experience increased thirst for water which would deplete this precious resource at a faster rate. There are some positive effects of radiation(weird, right? oxymoron! hah) which include a little better adaptation of the immune system and increased telomers’ length, which is related to cell-repair and aging. The longer, the better! The best way to protect the craft would be to build an artificial magnetic field around the craft, followed by some passive protection though layers of aluminium or metal hydrides. Astronauts would also have to carry exercise machines since the zero-g environment would reduce their bone density. ISS has 3(a bicycle, a treadmill, and a weightlifting machine of them on which astronauts workout for at least 6 hours a day. Their vision would also decline but that isn’t a huge problem as it could be corrected by power glasses. NASA revealed it’s newest space-suits for Artemis generation in 2019, so that’s a plus. The Exploration Extra Vehicular Mobility unit: Credit: NASA The Orion Crew Survival suit: Credit: NASA Both these suits are supposed to be the best NASA space-suits yet enabling extended deep space missions to Moon and Mars. So, can Starship or Orion even carry the resources for a Mars mission? According to NASA, a crew of six astronauts would require about 1360 tons of resources for a round-trip Mars mission! No rocket has that capability. So, cancel Mars and eat popcorns while streaming Netflix? Meh, not so fast! One possible way is to send astronauts with enough resources for the first six months, while simultaneously launching two more Starships with resources for while they’re on Mars and the returning journey or, maybe, sending astronauts in a crew of 3 on two Starships. Starship also comes with in-orbit fueling capacity. Engineers and mission designers would have thought on that, hopefully. During this pandemic induced lockdown of 2-3 months, people got frustrated and anxious and their state of mind was far from stable. Would you go into one for 21 months with no internet? Hell NO! Right? The astronauts traveling to Mars would. They would be in a live connection in the beginning but as the latency of communication increases with distance, it would be more of a passive communication mostly in the form of recorded messages. The psychological effects could be traumatizing for a normal human. Astronauts would think weird thoughts. We have seen the perspective of astronauts change when they return from space missions. They become more of a deep thinker. But thinking too deep may be the cause of anxiety and lack of motivation. They would question the objectives of the mission itself. Of course, we’re speculating the worst-case scenario. They’d still have movies, games, and other recreational activities. It is also really important for the astronauts to have a strong connection among themselves. In the end, it’s the endurance of the astronaut that matters as the other problems are in the process of being figured out by our awesome engineers. Once we’re on the Red Planet, it would be easy to build a colony as the technology for most purposes exists already. MOXIE is a Carbon Dioxide to Oxygen converting experiment, an experimental version of which is already aboard the Perseverance Rover launched last month! It could easily be scaled to meet our oxygen demands. Carbon Dioxide along with water-ice could also be used to make fuel for Starship after being extracted from the atmosphere. Caves, and other geographical structures along with inflatable modules could be our homes far from Earth. Though, due to radiation, we might have to go underground in the initial phase. Researches show that martian soil is perfect for making bricks. Just add some polymer plastics into soil and microwave it, and bricks are ready! Even some plants with modification to martian soil could be cultivated. And of course, the cost of Starship launches could come down to as low as $2 million per launch and as cheap as $2000 per Kg! It would make delivering food and other resources much feasible to our fellow earthlings on our sister planet. Even though nuking Mars is some way off, the technology for terraforming the planet exists, from placing a giant magnet in front of Mars in the L1 Lagrange point to melting polar ice caps! 1: Reduces Solar Wind stripping of the already thin atmosphere.Benefits of placing an artificial magnet: 2: Reduces High-Energy radiation to the surface. 3: Stabilizes the equilibrium of the atmosphere. We may not have a fully-fledged colony as long as the 2040s but the dream of being multi-planetary isn’t far off. We sure as hell would be multi-planetary before the end of the decade. Humans have always achieved their ambition. This one would mark the start of a new age, a new chapter in the history of mankind. We’ll show that the chance of living given to us hasn’t gone to vain. ... Read more...