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Data Science in Healthcare and Predictive Analysis of Diseases

How data science can prove crucial in tackling diseases using predictive algorithms designed to forecast chances of diseases and illness. Data is the new gold.

The World Wide Web has opened the floodgates to zeta-bytes of data to the common man. This humongous volume of data can be organized, analyzed, and then used for a variety of applications. Data can be mined and if properly handled, can have its worth multiple times more than gold. Among various domains, healthcare stands as a paramount receiver of the benefits that data science brings to the table.

With more and more people being introduced to smart watches and IoT wearables everyday, the number of users is exponentially increasing. These devices are churning out data at an unimaginable rate; data that can be harvested, and further used for research and study.

To understand data science, one must know the vital differences between data, information, and knowledge. Data alone is quite useless. Only after undergoing several processes does data become useful to us. Processed data becomes information. When this information is applied and further simplified, it gets converted into knowledge. Knowledge is the sword we carry in the practical world. Knowledge has real life implications. Therefore, data forms the foundation upon which entire theories and conclusions are built.

Data, just like humans, is a social animal. A unit of data makes little sense. It is when we start collecting it over a period of time, under various conditions and external parameters, it starts taking shape. Data scientists look for patterns in data over wide time frames and design predictive algorithms based on these patterns.

Data scientists working with data collected from IoT wearables analyse for instance, the heart rates of an individual over ten years. Other factors like food habits and sleep routines are monitored during this time. Once each set of data is processed, and relations are drawn between them, relative patterns are drafted and analysed. This data can then be used by medical experts to recommend lifestyle changes to the patient.

Similarly, it is possible that this practice of analysing an individual’s medical history can be translated to the level of a particular community, a state, or a country. Per exemple, the South Eastern states of the United States of America fall under the ‘kidney stone belt’. Populations residing in these states have a higher chance of developing kidney stones. A variety of reasons have been cited; higher mineral content in groundwater, higher level of meat protein in diets, and oxalate rich diets, are a few reasons. We are now aware of these facts due to meticulous work carried out by data scientists. People living in the South East U.S.A therefore can lead a preventive lifestyle minimising the chances of kidney stones.

Another excellent benefit of predictive analysis is the reduction in healthcare costs. Prediction of diseases that might occur in the next few years drives the person to take preventive measures early on. The old age adage, ‘Prevention is better than cure’, holds true here. Not only does mitigation measures stop life threatening diseases, it also eliminates the cost of treatments.

Going back to where we came from, its mind blowing when reminded that the source of all this knowledge and information is a wrist band with a few milligrams of silicon embedded in it. Next generation is looking into a future which is now only possible in sci-fi movies. A time where cyborgs coupled with autonomous AI brains now seems fast approaching. IoT wearables is only the first step. The future is limitless.