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Machine learning can precisely identify different cancer tumors at any stageby classifying cancerous and healthy samples based on their genomic profile. Wehave developed novel methods of MLAC (Machine Learning Against Cancer)achieving perfect results with perfect precision, sensitivity, and specificity.We have used the whole genome sequencing data acquired by next-generation RNAsequencing techniques in The Cancer Genome Atlas and Genotype-Tissue Expressionprojects for cancerous and healthy tissues respectively. Moreover, we haveshown that unsupervised machine learning clustering has great potential to beused for cancer diagnosis. Indeed, a creative way to work with data and generalalgorithms has resulted in perfect classification i.e. all precision,sensitivity, and specificity are equal to 1 for most of the different tumortypes even with a modest amount of data, and the same method works well on aseries of cancers and results in great clustering of cancerous and healthysamples too. Our system can be used in practice because once the classifier istrained, it can be used to classify any new sample of new potential patients.One advantage of our work is that the aforementioned perfect precision andrecall are obtained on samples of all stages including very early stages ofcancer; therefore, it is a promising tool for diagnosis of cancers in earlystages. Another advantage of our novel model is that it works with normalizedvalues of RNA sequencing data, hence people's private sensitive medical datawill remain hidden, protected, and safe. This type of analysis will bewidespread and economical in the future and people can even learn to receivetheir RNA sequencing data and do their own preliminary cancer studiesthemselves which have the potential to help the healthcare systems. It is agreat step forward toward good health that is the main base of sustainablesocieties.

Machine Learning Against Cancer: Accurate Diagnosis of Cancer by Machine Learning Classification of the Whole Genome Sequencing Data